dataesr/scientific-paragraphs-categorization huggingface dataset:
Background decomposition As an application of the light collection and veto model, we shall now present the results of the background decomposition of the HPGe energy spectrum recorded during physics data taking after the application of the LAr veto cut. This background model serves as a fundamental input for various physics analyses whose sensitivity is enhanced with the LAr veto background reduction. Previous work  [28]  has proven successful in describing the Gerda data in terms of background components, but before LAr veto and PSD cuts, referred to as "analysis" cuts. (pdfs) for various background sources have been produced with dedicated simulations of radioactive decays in the inner Gerda setup. A linear combination of these pdfs has been consequently fit on the first 60. 2 kg years data from Gerda Phase II in order to infer on the various contributions to the total background energy spectrum.
The reader is referred to [28, Figure  1 ] for a detailed documentation of the simulated setup. A variable binning is adopted for visualization purposes 123 Fig. 8  Background decomposition of the first 61. 4 kg years of data from Gerda Phase II surviving the LAr veto cut (black dots). The veto model is applied to the existing background pdfs before the cut  [28]  folding in the probability map ξ( x). Data before the cut is shown as a light blue filled histogram. Shaded bands constructed with the maximally distorted probability maps provide a visualization of the systematic uncertainty affecting each pdf Phase II data,  6  with a linear combination of background pdfs. Statistical inference is carried out to determine the coefficients of the admixture that best describe the data. In a Bayesian setting, posterior probability distributions of background source intensities resulting from the model before cuts are fed as prior information,  7  with the exception of 42 K.
Acknowledgements The Gerda experiment is supported financially by the  German Federal Ministry for Education and Research (BMBF) , the  German Research Foundation (DFG) , the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  Max Planck Society (MPG) , the  Polish National Science Centre (NCN , Grant number  UMO-2020/37/B/ ST2/03905 ), the  Polish Ministry of Science and Higher Education (MNiSW , Grant number  DIR/WK/2018/08 ), the  Russian Foundation for Basic Research , and the  Swiss National Science Foundation (SNF). This project has received funding/support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Grant  agreements No 690575 and No  674896. This work was supported by the  Science and Technology Facilities Council , part of the  U.
We showed that current and future social science research can facilitate the implementation of adaptation and can contribute to shaping a socially desirable and feasible adaptation future in coastal areas. Beyond social sciences, other contributions could be considered, including artistic activities or literature fiction, which although not a social science can greatly help to raise awareness and disseminate science. Based on this review, we suggest ways forward to amplify this effort and call for a strong development of coastal social science research. Specifically, we recommend to: expand the relatively limited scope of social research supporting coastal adaptation: this includes considering a wider variety of geographical contexts, coastal ecosystems and coastal management units; aim at better integrating emerging research areas identified above, while developing interdisciplinary studies linking social sciences with coastal science, climate services and the practice of coastal adaptation, which often remain focused on delivering climate and coastal hazard or risk geospatial datasets today.
Search strategy Data for this narrative review were identified by searches of PubMed (https://pubmed. ncbi. nlm. nih. gov), as well as references from relevant articles and reviews, using the following search terms: natural killer cells AND review/function/activation/migration/subset/dendritic cell/eosinophil/neutrophil/T-cell; natural killer cells AND asthma/lung/airway/lung disease; natural killer cells AND respiratory virus/influenza/rhinovirus/ respiratory syncytial virus/severe acute respiratory syndrome coronavirus 2 OR SARS-CoV-2; natural killer cells AND corticosteroids/asthma treatment. Only articles published in English from inception to January 2023 were included.
Asthma patients included in this study were atopic and were not treated with inhaled or oral steroids, suggesting they suffered from mild asthma. Neither NKG2D expression nor the function of NK cells were measured  [124]. Nevertheless, NKG2D expression on NK cells was found to be significantly increased in severe but not mild asthma outside of virus-induced exacerbation  [48]. Together, these data suggest that NK cells are dysregulated in asthma, conferring abnormal responses to RV infection that might lead to virus-induced exacerbation. In a murine model of asthma, intranasal administration of the Toll-like receptor 3 agonist poly(I:C) was used to mimic respiratory virus infection and to induce experimental exacerbation. Administered after OVA sensitisation and challenges, poly(I:C) further increased eosinophil and neutrophil numbers in BAL and mucus production in the airways.
2000; To l et al. 2006 ]. There are currently no approved medicines for treatment of OIC in children. The PK properties of naloxegol are well characterized in adult subjects and were shown to be similar in healthy adults and adult subjects who have OIC (AL-Huniti et al. , 2015;  Bui et al. , 2017). Single dose administration of naloxegol was shown to be linear over the range of 5 mg to 1000 mg in healthy subjects and from 5 mg to 50 mg in adult subjects who have OIC. Drug-drug interaction studies identi ed naloxegol as a substrate for CYP3A enzymes as well as the P-glycoprotein (P-gp) e ux transporter (naloxegol package insert). A Population PK (PPK) model containing data from Phase 1 studies conducted in healthy adult subjects and the Phase 2 studies conducted in adult subjects who have OIC was developed to characterize the PK properties of naloxegol in adults (Al-Huniti et al, 2015).
The study also applied an MIDD approach to estimate the appropriate dosing regimens for paediatric subjects with OIC. A PPK model was used to characterize the PK properties of naloxegol across the age range evaluated in the study. The PPK model comprised the PK data from adults contained in the prior PPK model (Al-Huniti et al, 2015) and from paediatric subjects enrolled in the SAFARI study. The PPK model was used to characterize the naloxegol PK properties in paediatric subjects after administration of oral tablets or oral solution of either a 12. 5 mg or 25 mg adult equivalent dose, based on a PBPK model  (Zhou et al. , 2016)  with allometric scaling.
Data assembly The SAFARI paediatric study and eight PK studies in adults used in the PPK model are described in Table  2. Naloxegol PK samples below the LLOQ were set to zero values and identi ed by a ag, BQLF = 1. These samples were excluded from the analysis  [Beal 2001; Ahn et al. 2008 ]. Only subjects under fasted conditions were included in the PPK analysis. The identi cation of potential outliers was performed by visual inspection of the raw dataset and the diagnostic plots (i. e. , direct value vs individual predicted concentration [IPRED]) generated from the base model. Descriptive statistics for baseline covariates are detailed as Supplementary Information (Table  S1 ).
Non-compartmental analysis Non-compartmental analysis was performed following the rst administration of naloxegol for subjects enrolled in Cohorts 1 to 4. The individual elapsed sampling times were used for the determination of plasma PK parameters of naloxegol. After the maximum plasma concentration (C max ), any measurable concentrations observed after ≥ 2 consecutive concentrations below the LLOQ were excluded from the PK analyses. One of the subjects in Cohort 1 was excluded from the analysis as their AUC value was at least 10-fold lower compared to other subjects in the same cohort. In addition, one naloxegol outlier data record of the paediatric study (ID = 40, TIME = 1. 0 hr) was excluded from the analysis because it was less than 10% of expected value. Approximately 1. 3% (43/3427) of naloxegol PK samples were below the LLOQ, leaving a total of 3384 samples from 296 subjects in the analysis dataset.
Neither the age or body weight were identi ed as signi cant covariates in the PPK model. Assessment of baseline body weight and age on CL/F and V1/F indicated a trend towards lower values in paediatric patients from 6 month to > 6 years of age. However, these values fall within the distribution of the rest of the paediatric and adult population (Fig. 5 ). A prediction-corrected visual prediction check (pcVPC) strati ed by volunteer subject and OIC patients was used to evaluate model performance with the purpose of determining whether the nal covariate PPK model is capable of reproducing the variability in the observed naloxegol data following naloxegol oral dose. The observed naloxegol data following naloxegol oral tablet or solution were generally contained within the 95% CIs of the model-predicted values, indicating that the model adequately described the observed data in adult volunteer subjects, adult subjects who have OIC and paediatric subjects receiving opioids.
A PPK model characterizing naloxegol plasma PK pro les of oral administration of either naloxegol solution or tablets was developed. Additional details of PPK model and its development are detailed as Supplementary Information. The concentration-time data from seven Phase 1 studies conducted in healthy adults, one Phase 2 study conducted in adult subjects with OIC, and one Phase 1 study in paediatric patients receiving opioid treatment were used for model development. PK data of naloxegol following oral administration of naloxegol solution or tablets were model-tted simultaneously. The dataset includes a total of 3,384 measurable naloxegol samples from 296 subjects. A two-compartment information criterion and residual error of Weibull absorption models were consistently lower than those of the transit compartment models.
MGDGs, DGDGs (digalactosyldiacylglycerols), and SQDGs (sulfoquinovosyldiacylglycerols) were the precursors for chloroplast lipids that formed symbiotic dinoflagellates thylakoid membranes  [24]. Although only MGDGs were detected in the differential analyses of the exposed and control corals, their relative concentration changes suggested chloroplast degradation. The concentration of two carotenoids (compounds 28 and 29) decreased significantly in response to 300 and 1000 µg/L of BM (Table  2 ). The molecular formulas of isomeric compounds 28 and 29 were C 39 H 48 O 6 (Calcd. m/z for C 39 H 49 O 6 + 613. 3524, 16 degrees of unsaturation). There are very few known natural products with this molecular formula. The most common is pyrrhoxanthin: a peridinin analog. Sirius annotated this compound as pyrrhoxanthin (60% CSI:FingerID score)  [16, 25] , but the experimental MS 2 spectrum of the protonated molecular ion of pyrrhoxanthin is not available in public databases.
Acknowledgments: We thank the  BIO2MAR  platform for providing access to instrumentation; a special thanks to  Karine Escoubeyrou  in this matter. We thank the  European Marine Biological Resource Center (EMBRC) at the Observatoire Océanologique de Banyuls, France , for providing access to the Banyuls aquarium facilities. Finally, the authors would like to thank  Rémi Pillot  and  Pascal Romans  ( Sorbonne Université ) for their help in developing the coral assay.
Data collection We examined retail prices for ACEi (enalapril, ramipril, and perindopril), ARB (candesartan and valsartan), BBs (bisoprolol, carvedilol, and metoprolol), MRA (spironolactone and eplerenone), ARNI (sacubitril/valsartan), and SGLT2i (dapagliflozin and empagliflozin). For metoprolol, we included metoprolol succinate; if unavailable, we included metoprolol tartrate. The medications chosen were those with a class I indication for use in contemporary HFrEF guidelines based on a reduction in mortality or the composite of mortality and readmission. 3 nline drug repositories such as those accessed via the World Health Organization do not include data on contemporary HFrEF therapies. Thus, we collected retail price data from three online pharmacies per country, chosen by convenience sampling.
Countries included in survey The analysis included four high-income (United States of America Canada, England, and Norway), three upper-middle income (Peru, South Africa, and Iran), two lower-middle income (Pakistan and Bangladesh), and one low-income country (Uganda). Syria, Togo, and Equatorial Guinea (low-income countries) were randomly and sequentially chosen for inclusion in the survey but had to be excluded as online data were not available and no responses were received from community pharmacies. Online pharmacies were available in all included countries other than Iran and Uganda, where data were obtained from community pharmacies. In South Africa, one online pharmacy and two community pharmacies were included as only one online pharmacy was available.
Limitations Our sample was restricted to 10 countries. We only listed prices for the lowest doses of each agent and our analysis focused on retail prices paid by patients; the prices do not represent the cost incurred by pharmacies or amounts paid by patients enrolled in publicly funded or private insurance programmes. While we used data from online pharmacies, the additional physical pharmacies surveyed in each country were from the capital city; prices may have been be higher and access to drugs poorer in rural locales. Our survey of prices was limited to private pharmacies, as price data from public pharmacies were unavailable. Although we adjusted for the dose of medication in our cost analysis, not all pharmacies queried offered the same dosages of medication. Our analysis of affordability relied on the GNI per capita rather than household or disposable income as an established reference.
Calculation model FLAC 3D finite element software is adopted to establish the numerical calculation model of the new foundation pit as well as the existing station with the PBA method. To decrease the impact of boundary effects and consider the model range as well as the calculation accuracy, the model dimension is set as follows: length (Y direction)×width (X direction)× height (Z direction)= 96m×180m×90m. The overall model has a total of 1624,035 nodes and 1619,690 units. The calculation model as well as the grid division diagram is displayed in Fig. 2. Normal constraints are set around the model, and fixed constraints are set at the bottom of the model, and the top surface sets to be free  (Vali et al. 2020; Fang et al. 2022). Only the self-weight stress is considered for the initial geo-stress balance.
Side wall of the subway station The variation law about the principal stress of the side wall of the existing subway led by the construction of foundation pit under stage A and stage D is displayed in Fig. 13. In FLAC3D software, the negative stress values represent the compressive stress, while the positive values refer to the tensile stress. In Fig. 13 , when the construction of the foundation pit is in stage A, both the minimum and maximum principal stresses of the side wall are negative, that is, the overall compression and the largest compressive stress of the two are 1. 488MPa and 0. 545MPa respectively. After constructed to the foundation pit base (i. e. , at stage D), the peak value of the minimum principal stress of the side wall reaches 1. 455MPa, which is reduced by 0. 033MPa compared with the stage A.
Material And Methods Experimental settings: We collected subsurface water samples using a Van Dorn water sampler at three different sampling points: (P-01) located 18 km upstream of the reservoir, in the city of Queluz, São Paulo (State); (P-02) located in Funil Reservoir; and (P-03) located 6 km downstream of the reservoir, in the city of Itatiaia, Rio de Janeiro (State). In situ, physicochemical data were measured using a probe (Hanna Instruments -HI9829). Then, water samples were correctly packed into Te on asks and transported to the laboratory in ice coolers. In the laboratory, water samples were ltered through cellulose acetate membranes (0. 45 µm) to obtain the respective dissolved (< 0. 45 µm) and particulate fractions (> 0. 45 µm). After the ltration step, ion concentrations in dissolved fractions were determined using Ion exchange chromatography (Dionex ICS-2000 Ion Chromatograph with Dionex AS40 Automated Sampler).
Acknowledgements The authors thank the  Department of Geochemistry from Universidade Federal Fluminense (UFF) ;  FURNAS Centrais Elétricas  for sampling authorization; Labáguas ( PUC-Rio ) for conducting chemical analysis;  Professor Dr. R. C. Campello  by having provided the use of microwave oven;  Professor Dr. B. A. Knoppers  by having provided the use of spectrophotometer; Ludmila for her help in chemical analysis; and  CNPq for Post-Doctoral Grant. The authors thank the  UFSB  for the support granted through the  PROPPG/UFSB  edict  Nº 07/2022 , Process  23746. 002054/2022-52. Author contribution statement:  J. M. C. O. S. N. designed the study and prepared the gures and tables to present the data. J. M. C. O. S. N. , S. R. P. and D. F. A. wrote the main text of the manuscript. All chemical analyses were performed by  J.
More specifically, the pooled point estimate for dose decreases was 9. 5% (95% CI 8. 7e10. 3) in the RCTs  [39]  and in 25. 4% of 33,962 ART cycles in a real-world setting  [40]. Finally, the BMI of the women included was relatively low (23. 9 [SD 3. 8] kg/m2 in the 150 IU/day dose group vs. 23. 8 [SD 3. 6] kg/m2 in the 100 IU/day dose group); therefore, data from the OPTIMIST trial may not apply to overweight/obese patients at risk for high ovarian response  [26]. The benefits of a reduced starting dose on OHSS incidence were reported in an analysis of four randomized controlled trials (RCTs) in the Cochrane review by Lensen et al. [41]. Two of these studies recruited a general population of women undergoing IVF/ICSI and reported OHSS following fresh embryo transfers after hCG triggering [27] or frozen embryo transfers after GnRH agonist triggering  [42] ; one study recruited normo-ovulatory women and reported OHSS after fresh transfers  [43] , and one study recruited hyper-responders and reported OHSS after either fresh or frozen embryo transfers  [26].
| Study limitations SCAN represents the biggest global compendium of NET data extant and assessed patient and HCP perspectives regarding NET care. Almost 3000 patients and HCPs responded; however, caution is needed when interpreting results for Africa and South America due to small sample sizes. Voluntary response sampling allowed for efficient data collection, but potentially resulted in group numbers and demographic and geographic imbalances. For example, many respondents were well-educated and of higher income, and the use of an online survey tool might have created difficulties for some patients. HCPs were from a wide variety of specialties and approximately one-fifth of HCPs reported no experience in diagnosing and treating NETs. Concerning time to diagnosis, the fact that incidental diagnoses cannot be included is a limitation.
The methodology used did not appear to impact the age profile of the sample with the age distribution being similar to that of other registries. 5, 21, 22 Voluntary sampling may also introduce bias by reflecting highly engaged respondents. Due to the relatively small number of respondents from certain regions, there are clear discrepancies between Asia and Europe/NA/Oceania and between EDE and AE, in favor of Eu/NA/Oc and AE, respectively. Also, the completion of every question was not compulsory, and so the number of respondents per single question is unknown, which is a potential limitation. However, these demographic features do not appear to have a significant impact on the explorative data results, such as, NET type, tool usage, diagnosticians used, issues and recommendations. Therefore, despite limitations and demographic biases, SCAN provides robust NET data that are aligned across NET types and continents, and highlights continued unmet needs alongside invaluable recommendations from patients and HCPs for improving NET healthcare.
ACKNOWLEDGMENTS The  International Neuroendocrine Cancer Alliance (INCA)  would like to thank all its members as well as its partners:  ENETS (European Neuroendocrine Tumor Society) ,  NANETS  ( North American Neuroendocrine Tumor Society ),  APNETS (Asia-Pacific Neuroendocrine Society), CommNETs (Commonwealth Neuroendocrine Tumor Group), JNETS (Japan Neuroendocrine Tumor Society) ,  CNETS  ( Chinese Neuroendocrine Tumor Society ),  UICC (Union for International Cancer Control) ,  EURORDIS (European Organization for Rare Diseases) ,.
External quality control An external quality control program for thiopurine nucleotides was proposed by SKML society (www. skml. nl). RBC lysates samples were sent to international laboratories which subscribed to this program. A comparative assay was performed in our laboratory as follow: RBC lysates (n = 6) from SKML, were analysed using both 6-TG as calibration standard conformly to our original method published in Clinical Chemistry in 1998 and considered as one of the international reference method and also 6-TGR as calibration standard. The data were compared to the target value of our original method.
Most CADe systems described in the literature are trained with relatively small data sets originating from a single center. This limits their generalizability and results in suboptimal performance when applied in daily practice. 9 In addition, most of the currently developed CADe systems require significant computational resources, which limits real-time video-based application and hampers efficient integration in existing endoscopy systems. Our consortium envisions to develop a robust and 'ready-for-use' CADe system for the detection of early Barrett's neoplasia in a stepwise manner, using efficient and generic software that enables easy integration in the endoscopy suite. In this paper, we aim to describe the research infrastructure of our consortium and to report the first results of CADe performance on still images after training the algorithm on retrospectively collected imagery.
Data collection To effectively train our CADe system, we envisioned to create the largest Barrett imagery data set that is currently described. To this end, both retrospective and prospective data were collected by collaborative partners in our consortium. Retrospectively recorded endoscopic images were collected from the endoscopic databases of the participating centers that included patients under surveillance for their non-dysplastic Barrett's esophagus (NDBE) or undergoing endoscopic treatment of BE neoplasia. Endoscopic images recorded with Olympus 100-series endoscopes (H180 and HQ190) and processors (CV-180 and CV-190; Olympus, Tokyo, Japan) between 2012 and 2021 were selected. The images were automatically extracted from the system and de-identified using proprietary software specifically designed for this project.
The de-identification software was developed to detect text (i. e. , patient identifiers) within imagery and subsequently place a black box over the text, followed by automatic overwriting of the original image. Any meta-data was automatically removed as well. The majority of the participating centers also collected prospective imagery following a standardized image acquisition protocol. In this protocol, endoscopic images are collected in WLE in both NDBE and neoplastic patients. Endoscopic images were recorded for each 2 cm of the Barrett's segment. All imagery was obtained in overview without specific focus on areas of interest such as potential neoplastic lesions, if present. For an extensive description of the prospective data acquisition protocol, please see the supplementary materials.