dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 00035. L, PI: Kohno). We thank Matthieu Béthermin for helpful discussions. This paper benefited greatly from a helpful report from an anonymous referee. This paper makes use of the ALMA data from the program 2016. 1. 00324. L. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), NSC and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and NAOJ. R. J. B. , M. S. , and T. N. acknowledge support from NWO TOP grant TOP1. 16. 057. J. G-L. acknowledges partial support from ALMA-CONICYT project 31160033. I. R. S. acknowledges support from STFC (ST/P000541/1). F. W. and M. N. acknowledge support from ERC Advanced Grant 740246 (Cosmic Gas).
U. D. acknowledges the support of STFC studentship (ST/R504725/1). D. R. acknowledges support from the National Science Foundation under grant numbers  AST-1614213 and AST-1910107    a This column presents stack results. Each source is weighted according to the inverse square of the noise. The weightings are therefore independent of stellar mass and U V -continuum slope β. b Both the bootstrap and formal uncertainties are quoted on the result (presented first and second, respectively). c The 1. 2 mm continuum flux predicted using the M99 or SMC IRX-β relationship weighting individual sources in exactly the same way as for the measured 1. 2 mm continuum flux, so these two quantities should be directly comparable. d Assuming a standard modified blackbody SED with our evolving dust temperature model and accounting for the impact of the CMB on the measured flux  (da Cunha et al.
D. SENSITIVITY OF IRX VS. STELLAR MASS RELATION TO STELLAR POPULATION MODEL While exploring the relationship between IRX and stellar mass, we experimented with the use of different codes to estimate the stellar mass for individual sources over our HUDF ASPECS field. As found e. g. in Appendix A, stellar population codes like MAGPHYS  (da Cunha et al. 2008)  and Prospector  (Leja et al. 2017 ) find ∼0. 12 dex higher stellar masses in general than the FAST  (Kriek et al. 2007 ) stellar population we use for our fiducial stellar mass estimates. Our determination of the IRX vs. stellar mass relation can potentially depend on the stellar population modeling code we use to estimate the stellar masses of specific sources. To investigate the dependence on the stellar mass estimates, we made use of the stellar mass estimates that  Leja et al.
METHODS We produced analogue haze particles in an atmospheric chamber under theoretical super-Earth and mini-Neptune conditions. We then collected the solid sample produced in this experiment and performed very high resolution mass spectrometry with a Thermo Fisher Scientific LTQ-Orbitrap XL mass spectrometer. We also performed elemental combustion analysis to provide a starting point for the compositional study in order to identify specific molecules. Once measurements were taken, we used custom IDL software, idmol, to analyze the data and make molecular identifications. A detailed summary of each step in our procedure follows.
These data contain many hundreds to thousands of peaks, making manual identification impractical. As such, data were analyzed with custom IDL/FORTRAN software, called idmol  (Hörst 2011) , which quickly assigns molecular peaks. First, idmol calculates all possible molecules from the mass spectrum and then narrows down the options based on user input parameters such as the maximum number of oxygen molecules, the mass tolerance, and the nitrogento-carbon ratio. The program then eliminates peaks that are below the noise level or due to Fourier ringing in the most intense peaks  (Hörst 2011). Idmol uses the nitrogen rule (i. e. , that compounds with an even nominal mass have an even number of nitrogen atoms and vice versa for compounds with odd nominal masses) to make assignments for lower mass peaks and then assigns likely higher mass peaks based upon its previous lower mass assignments.
Assigned Table  3. For samples produced by UV illumination, elemental ratios and associated carbon-to-oxygen and carbon-to-nitrogen ratios. Errors reported are the standard deviation of all mass ranges and both polarities for each sample. Some plasma products were insoluble and unable to provide adequate signal for measurement and analysis. The corresponding UV samples also had very poor signal and attempts at analysis were inconclusive. Compositional differences between the samples produced by different energy sources exist, but mostly fall within error. molecules are then compared against a database of known molecular formulas for prebiotic material, including amino acids, nucleobases, and simple sugars taken from the literature (e. g. ,  Lu & Freeland 2006; Cooper et al. 2018). Once formula assignments were made by idmol, we checked each assigned peak in the sample against the corresponding blank to ensure any potential contamination was accounted for in the sample.
Solubility of Exoplanet Haze Analogues As shown in Figure  3 , the exoplanet haze analogues produced in the laboratory with the plasma energy source exhibited diverse solubility behavior. Quantitative measurements of solubility were outside the scope of this study. Additionally, there are various inconsistencies in the literature about protocols used to determine solubility of the complex mixtures often referred to as "tholin" (for discussion see e. g. ,  Carrasco et al. 2009; He & Smith 2014). Instead our solubility metric was qualitative, determined both by visual inspection of the sample within the solvent as well as visual inspection of the resulting mass spectral data (which can be affected by other chemical properties). During visual inspection of a sample in a solvent, we noted any color change and any visible decrease in the amount of solid.
ACKNOWLEDGMENTS S. E. Moran was supported by  NASA Earth and Space Science Fellowship  Grant  80NSSC18K1109. Portions of this study were supported by  NASA   Exoplanets Research Program  Grant  NNX16AB45G. C. He  was supported by the Morton K. and  Jane Blaustein Foundation. This work is supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes  and the  French Space Agency (CNES)  under their Exobiology and Solar System programs. Cédric Wolters acknowledges a PhD fellowship from  CNES/ANR  ( ANR-16-CE29-0015 2016-2021 ) S. E. Moran also thanks the entire  Hörst PHAZER lab group  and the STARGATE collaboration for their useful discussion and support on this work, as well as two anonymous referees whose careful review improved this manuscript.
A post hoc analysis of each mixed model was graphically performed with quantile-quantile plots on the fixed effects and on the random effect  [26]. Mixed models were performed using the package "lmerTest"  [27]. ANOVA (without random effect) were conducted for the period duration, the number of lactations, the peak of lactation, and the milk yield parameters, to observe the effect of the three dietary treatments. If the results of the mixed models and ANOVAs were significant (p ≤ 0. 05), a Tukey test was performed to compare the average pairwise, using the "emmeans"  [28]  and "multcompView"  [29]  packages for mixed models and "agricolae" package  [30]  for ANOVAs. p > 0. 05 and ≤0. 10 were considered as trends. According to the milk or blood traits considered in this study, the time step considered was the week.
Also, in France, we lack data regarding national prevalence, impact on QOL, perception and awareness of the condition in women, and regarding their actual management, own evaluation of health system and their expectations. We therefore conducted a large nationwide study aiming to specify the exact prevalence of dysmenorrhea and also to have a descriptive analysis of this symptom and its consequences and management, in order to provide actionable insights to determine areas for improvement for information, diagnosis and treatment. The content of the questionnaire was constructed with the steering committee of the study. The questionnaire collected data regarding demographics, gynecological history, presence and perception of menstruations and pain, evaluated on a visual analog scale (VAS) from 0 to 10, care pathway, women's evaluation of actual medical care, impact of dysmenorrhea on daily life and QOL (using the Short-Form 36), and their expectations in particular regarding medical information.
Methods Except for the Short-Form 36, statistical analyses were performed using COSI software. Descriptive statistics include frequency tables, mean and standard deviations. Regarding the Short-Form 36 analysis, normal distribution of the continuous variables was assessed using the Shapiro-Wilk Test. Non parametric tests were used to compare the Physical Health Component Scores (PHCS) and Mental Health Component Scores (MHCS) between the groups (Kruskal-Wallis). A p value<0. 05 was considered as significant. Statistical tests were performed using SPSS Statistics 24 (IBM, 2016, Chicago, IL, USA).
Daily impact and QOL (Short-Form 36) Data regarding physical and psychosocial impact are presented in Table  3. Quantified mean impact score of dysmenorrhea was quite weakly rated on average. On the other hand, the daily impact was rated as important/very important for 67% of the women currently having dysmenorrhea for fitness and 50% for sexual life. We also measured that 22% of women currently affected by dysmenorrhea had missed at least one day of work within the last 12 months (average duration of work absence=1. 2 days±4. 5). Regarding QOL measured by the Short form SF 36 Questionnaire, both the Physical Health component scores and the Mental Health component scores were significantly reduced in the subgroup of women currently affected by dysmenorrhea versus women that were never affected as exhibited in Table  4.
Background: Nonalcoholic fatty liver disease (NAFLD) has reached high prevalence, paralleling the obesity pandemic. The aggressive form of the disease, nonalcoholic steatohepatitis (NASH), is characterized by fatty infiltration and inflammation of the liver, can progress to compensated cirrhosis (CC) and endstage liver disease (ESLD: decompensated cirrhosis [DCC] and hepatocellular carcinoma [HCC]), and may ultimately require liver transplantation (LT). Real-world data on the burden of NAFLD/NASH are limited. This study aimed to evaluate the clinical and economic burden of NAFLD/NASH to the French hospital system. Methods: This retrospective cohort study used data from the French PMSI-MCO database. Adults with NAFLD/NASH diagnosis identified between 2009 and 2015 were categorized into disease severity cohorts (NAFLD/NASH, CC, DCC, HCC, and LT).
In one modeling study, the economic impact of NAFLD in France was estimated as €11. 4 billion ( €784 per patient) annually, with inpatient (hospitalization) costs as the primary cost driver  [15]. This study was informed by limited realworld data and thus required several modeling assumptions. With these limitations, data gaps regarding diagnosed NASH patients' natural history and economic burden in the French pop-ulation require further evaluation. Health Technology Assessment (HTA) organizations strive to improve medical decision making by leveraging real-world evidence from large health system, which generally provide data on large real-world populations over extended observation periods  [ 16 , 17 ]. These database studies allow for an increased understanding beyond clinical trials and small cohort studies on patients' health outcomes and healthcare utilization subject to real-world clinical practice patterns.
Study design and data source This was a retrospective, observational cohort study using data sourced from the French computerized hospital database for medicine, surgery and obstetrics (PMSI-MCO: Programme de Médicalisation des Systèmes d'Information -Médecine, Chirurgie et Obstétrique ) from 1 January 2009 to 31 December 2015. The PMSI-MCO database is a comprehensive record of all hospitalizations in France (including public and private hospitalizations)  [ 18 , 19 ]  and includes over 30 million public and private admissions per annum (outpatient encounters not included). Discharge diagnoses are coded in the PMSI-MCO database using the International Classification of Diseases, 10th Revision (ICD-10), which was implemented in France starting in 1997. Medical procedures are coded using the national Common Classification of Medical Acts ( Classification Commune des Actes Médicaux ).
Study sample selection criteria Previous studies have reported that NASH is underdiagnosed and thus under-coded, likely owing to a lack of available treatments. As such, both NAFLD-and NASH-diagnosed patients were included in the study in a combined cohort. Patients who were 18 years of age and older, with at least one claim with a diagnosis code for NASH and/or NAFLD (ICD-10 K75. 8, K76. 0), and who were identified in the PMSI-MCO database between 1 January 2009 and 31 December 2015 were included in the study. The first diagnosis of NAFLD/NASH marked the index date. At any point during the study period, patients with other causes of chronic liver disease were excluded as were patients infected with human immunodeficiency virus. Patients were required to have data available for at least 6 months preceding the index date.
Study outcomes Demographic data extracted from the database included age and sex. Medical data extracted from the database included number of hospital stays, the Diagnosis Related Group (DRG) for each hospital stay, length of stay and ICD-10 diagnosis. Comorbidities based on an ICD-10 diagnosis -HTN, hyperlipidaemia, obesity, CVD, sleep apnoea, DM, renal impairment -were also extracted from the database. Economic data included inpatient care costs (annual costs per patient) directly extracted from standard cost estimates completed in the PMSI-MCO database. Inpatient healthcare costs are reimbursed by the French national health insurance body to hospitals through a national DRG-based payment system. The DRG of a hospital stay is determined based on the recorded discharge diagnosis and classifying procedures performed during the stay.
Demographic and clinical characteristics In total 49,721,264 unique patients were included in the PMSI-MCO database between 2009 and 2015 and 186,956 patients (0. 4% of the population) had a diagnosis code for NAFLD/NASH. Of these patients, 131,656 met the inclusion criteria for the study (NAFLD/NASH patients without exclusionary diagnosis). Median follow-up was 34. 8 months (interquartile range: 15. 6-57. 6). Over the full study period, 125,052 patients had a diagnosis of NAFLD/NASH and experienced no further progression; 1491 (1. 1%) had a diagnosis of CC; 7846 (5. 9%) had a diagnosis of DCC; 1144 (0. 9%) had a diagnosis of HCC; and 52 (0. 04%) underwent LT. A total of 6967 patients (5. 3%) died within the study period ( Fig. 1  ). The mean (standard deviation [SD]) age of the patients with NAFLD/NASH at index diagnosis was 55.
Discussion By characterizing and longitudinally following for up to 7 years over 10 0,0 0 0 patients in the PMSI database, which has been used extensively to provide real-world evidence to HTAs and medical decision makers  [20]  , this retrospective, observational cohort study of all hospitalized patients with a NAFLD/NASH diagnosis in France between 2009 and 2015 addresses important data gaps in our understanding of the disease. Specifically, this study provides important evidence on patient identification patterns in real-world clinical practice, natural history of the disease in diagnosed patients, and the substantial impact of NASH and its progression to severe liver disease on patients' hospitalization resource utilization and costs. Unlike other French healthcare databases, which provided data on a limited sample of a population to inform generalizations to a broad population, the PMSI records every inpatient admission in the country and thus extrapolation of our findings from a sample to a population is not needed.
Certain comorbid conditions in patients with NAFLD/NASH were also associated with increased costs when controlling for patient demographics and liver disease severities, indicating that the cost burden associated with NASH also extends to extrahepatic conditions. Specifically, CVD and renal impairment were associated with the largest cost increases. However, an unexpected finding in the study was that DM and hyperlipidaemia were associated with a negative impact on costs. Previous research has suggested that treatments for these conditions, such as insulin sensitizers (e. g. pioglitazone) and statins, may have extended treatment effects on the liver, leading to a slowing of NASH disease progression  [ 37 -41 ]. A key strength of this study is the large population-based, longitudinal dataset that has been utilized, which includes all hospitalized French patients during the study period.
To our knowledge, this study represents the first assessment of NAFLD/NASH patients' real-world economic and clinical outcome in France. Longitudinally following patients for up to 7 years provided sufficient time to evaluate disease severity progression and associated changes in economic burden. Certain limitations should be acknowledged that were inherent in the PMSI database. Calculated resource utilization and costs are also all-cause figures, rather than relating to a specific diagnosis, treatment, and management of comorbidities. In addition, costs incurred outside a hospital setting, including all outpatient costs, were not captured and patients with NAFLD/NASH concomitant with other aetiologies of liver disease were excluded from the study; therefore, the hospitalization resource utilization and costs estimated in this study are likely to be an underestimation of the true economic burden.
Further evaluations of the NAFLD diagnosis patterns in routine clinical practice and characterizations of the clinical and economic burdens of the disease outside of the hospital setting are needed to fully elucidate the impact of the disease to the French health system. The data may be affected by database entry errors and misclassification. Patients with NAFLD/NASH alone who were included in our analysis might include F0-F3 patients as well as undiagnosed F4 (compensated cirrhosis) patients due to under-coding and the lack of specific ICD-10-CM codes for F0-F3. We thus could not granulize rates of progression by fibrosis stage and variations may exist in the composition of fibrosis score distribution by database. The apparent under-diagnosis of NAFLD likely led to this study providing overestimations of the true risks of progression and mortality.
Furthermore, the observation of NAFLD underdiagnosis highlights the need for treatment guidelines to consider a recommendation of broad NAFLD screening with validated identification algorithms. Such screening algorithms, if based on age and combinations of obesity and metabolic commodities observed in this study to be associated with NAFLD, may also be utilized in future database evolutions to identify and characterize undiagnosed NAFLD patients and cryptogenic cirrhotic patients with likely NAFLD etiology of liver disease. In addition, owing to the nature of the PMSI database, patient mortality that occurred outside of the inpatient setting was not recorded leading to the potential for underestimation of the true risk of mortality by disease stage. In conclusion, this study of the French hospitalized population found a high comorbidity and economic burden associated with NAFLD/NASH and a high risk of disease progression and mortality.
Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo-Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0. 16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data.
If we focus on high resolution multispectral (MS) imagery, such as Spot 6-7, Landsat 8, or Sentinel 2, a range of spatial and spectral resolutions is available, with potential to improve signal sensitivity to AGB. Spot 6-7 delivers 1. 5 m high-resolution products with five spectral bands. The Landsat 8 sensor provides six spectral bands with a spatial resolution of 30 m. Sentinel 2 offers ten spectral bands at refined 10 m spatial resolution. It is not clear however which satellite data (between broad spectral bands and relatively high spatial resolution, i. e. , Spot 6-7, or narrower, more numerous spectral bands and lower spatial resolution, i. e. , Landsat 8 and Sentinel 2) provides the best solution for upscaling AGB from field data. Another issue is that the cost of field data acquisition of sufficient quality generally leads to a poor calibration of AGB prediction models.
At the forest-savanna transition zone, complex mosaics of contrasting land cover and land use types exacerbate the issue, making the precise quantification of AGB stocks and stock change particularly difficult. To overcome this problem, several authors  [7, [11] [12] [13] [14]  have recommended a multi-step upscaling approach based on airborne Light Detection And Ranging (LiDAR) scanning (a. k. a. ALS). Due to its ability to accurately characterize the vegetation's three-dimensional structure, ALS has indeed emerged as the reference technology for mapping vegetation AGB variations at landscape scales  [12, [15] [16] [17] , although cost still prevents wall-to-wall mapping at regional or national levels. The currently held assumption seems to be that the calibration of AGB mapping models based on MS imageries is improved when using the larger calibration dataset allowed by an ALS sampling of the territory  [7, 18].
3 and 4, respectively). Details of cross-validation strategies can be found in the original studies. RMSPE provided in Mg of carbon per hectare in original studies are converted to Mg of AGB per hectare with a carbon-to-AGB conversion factor of 0. 5. In contrast to this model-based approach to AGB extrapolation, a design-based approach remains well-accepted, notably recommended in the Intergovernmental Panel on Climate Change (IPCC) guidelines for green-house gas inventories  [25]. In this approach, average AGB densities (referred to as emission factors in this context) are attributed to each land cover class. A precise estimation of these AGB densities requires rigorous sampling, for instance via a national forest inventory  [26] , which is still lacking in many tropical countries. This then imposes the need to resort to global database values for attributing AGB densities to each land cover class, resulting in lower confidences in estimates (Tier 1).
This then imposes the need to resort to global database values for attributing AGB densities to each land cover class, resulting in lower confidences in estimates (Tier 1). In the context of a complex landscape mosaic in Cameroon, our objective was to: (i) test if intermediate ALS sampling allows improvements to large scale AGB estimations in model-based or design-based approaches; and (ii) test if the choice of multi-spectral satellite sensor or predictors can improve signal and predictive power in the case of model-based approaches.