dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015; Kisel, McInnes, Toomey, & Orme, 2011; Rakotoarinivo et al. , 2013; Rosenzweig, 1995). Therefore, under the climate stability hypothesis, we expect a positive association between metrics of long-term climatic stability and tree palm relative abundance. In this study, we use a large pantropical dataset of forest plots to quantify global-scale variation in tree palm abundance (quantified as the basal area and the number of stems) relative to co-occurring trees. We examine spatial patterns of tree palm relative abundance across major biogeographical realms, in addition to correlations with abiotic and biotic variables in light of the contemporary conditions and climate stability hypotheses outlined above. As a step towards assessing the potential ecosystem-level consequences of tree palm relative abundance patterns, we estimate the amount of error introduced to standard calculations of above-ground biomass (AGB) when tree palms are pooled with other trees versus treated separately.
| Forest inventory data Our analysis is based primarily on data from ForestPlots. net (Lewis et al. , 2009; Lewis et al. , 2013; Lopez-Gonzalez et al. , 2009; Lopez-Gonzalez, Lewis, Burkitt, & Phillips, 2011; Malhi et al. , 2002) , which integrates data from research plot networks active in the Neotropics (RAINFOR and PPBio), Africa (AfriTRON) and Southeast Asia (T-FORCES), in addition to other networks and researchers, and also uses the pan-tropical Gentry 0. 1-ha transect dataset  (Phillips & Miller, 2002)  and the database compiled by  Slik et al. (2018). We assembled data for 2,624 individual forest plots located in the subtropical and tropical moist broadleaf forest biomes as defined by  Olson et al. (2004). A list of publications associated with data used in this paper is found in the Supporting Information Appendix S1.
In each plot, all individual stems with a diameter of ≥10 cm in diameter at breast height (d. b. h. , 1. 3 m above the ground) were identified and measured for d. b. h. Our analyses focus on arborescent palms that reach ≥10 cm d. b. h. (henceforth, "tree palms"), because smaller-diameter stems are excluded in the standard protocol of most forest inventories. Although palms occur in a wide range of tropical ecosystems (including rain forests, savannas and dry forests), we focused on moist forests because this biome houses the greatest palm diversity and it is where the majority of plots in our dataset are located. We restricted our analyses further to plots reported as "old-growth", "primary" or "undisturbed" by the original data collectors and excluded 57 plots described as swamps or "monodominant" palm forests.
We analysed a total of 2,548 plots (covering a total of 1,191 ha; Supporting Information Appendix S2). To reduce spatial autocorrelation, we pooled data from plots within the same 10 km × 10 km grid cell; hereafter, we refer to these as aggregated plots as "locations". After aggregation, our dataset included 842 locations (Figure  1a ). The area sampled per location ranged from 0. 1 to 51. 8 ha (median ± SD = 0. 4 ± 3. 5 ha), with 95% of the locations sampled were ≥0. 1 ha. We assigned each location to one of the biogeographical realms defined by  Olson et al. (2004) , but we combined locations in Oceania with Australasia because of their strong historical connection  (Muellner, Pannell, Coleman, & Chase, 2008)  and their relatively low sample sizes (n = 32 and 37, respectively). For each location, we calculated the relative basal area (BA palm ) and the relative abundance (RA palm ) of tree palms as the sum of the tree palm basal area or number of tree palm individuals divided by the total basal area or total number of stems.
(a) (b) we used several variables that are likely to have had major impacts on the distributions of palms and their habitats (e. g. ,  Melo, Freitas, Bacon, & Collevatti, 2018). Specifically, we calculated the absolute difference (or "anomaly") between the LGM (21,000 years ago) and modern climatological averages , for both the mean annual precipitation (in millimetres per year) and the precipitation in the driest quarter (in millimetres per quarter) using 30 arc s (c. 1 km 2 ) resolution data from CHELSA  (Karger et al. , 2017). We used LGM variables based on data from the Palaeoclimate Modelling Intercomparison Project (PMIP3) and output from the Community Climate System Model (CCSM4)  (Karger et al. , 2017). We used several datasets to address our hypothesis about contemporary ecological controls on tree palm relative abundance (the contemporary conditions hypothesis).
For contemporary climate, we extracted the mean annual precipitation (in millimetres per year) and precipitation in the driest quarter (in millimetres per quarter) for each location from the 30 arc s (c. 1 km 2 ) resolution CHELSA dataset  (Karger et al. , 2017). For edaphic conditions, we extracted cation exchange capacity (CEC; a general proxy for soil fertility; cmol+ kg-1 ) at 250 m resolution from the SoilGrids website (https://soilg rids. org/). We focused on CEC because prior work has shown associations between forest composition (including palm diversity) and soil fertility  (Muscarella et al. , 2019). We extracted the depth to the water table (in metres) from the 30 arc s (c. 1 km 2 ) resolution database of  Fan, Li, and Miguez-Macho (2013). We examined how conditions at the study locations reflect the range of conditions in their respective biogeographical realms by sampling each variable at 10,000 random points in each realm (Supporting Information Figure  S1 ).
We extracted values for the predictor variables listed above based on the mean latitude and longitude of plots in each location. We calculated two proxies for turnover rates in each location. First, because forests with faster turnover rates tend to have shorter canopies  (Feldpausch et al. , 2011) , we estimated the maximal canopy height for each location based on the tree inventory data. More specifically, given that most plots do not include measured data on tree height, we used the "BIOMASS" package (Réjou-Méchain, Tanguy, Piponiot,  Chave, & Hérault, 2017)  to estimate the height of each individual tree based on its diameter using the geographically based allometric equation of  Chave et al. (2014). We used the 95th percentile of estimated tree height in each location as a metric of maximal canopy height (comparable results were obtained when using the 99th percentile).
Second, we calculated the basal area-weighted mean (CWM) wood density for each location by matching wood density data from the global wood density database  (Chave et al. , 2009; Zanne et al. , 2009)  with the relative basal area of each species in each location. The CWM wood density reflects life-history strategies of trees, and stands with lower values of CWM wood density tend to have more rapid turnover rates  (Chave et al. , 2009; Phillips et al. , 2004 Phillips et al. , , 2019)). Given that we were interested in the relationship between local environmental conditions and palm relative abundance patterns, we excluded palms when calculating CWM wood density. However, note that CWM wood density values per location were highly correlated whether or not palms were included (Pearson's r =. 98).
We fitted models using STAN (Stan Development Team, 2016) via the "brms" R package  (Bürkner, 2017)  and with default (uninformative) priors (see model code in the Supporting Information Appendix S3). We used four chains with 1,000 burn-in samples and 1,000 sampling iterations per chain. All parameters had R values < 1. 1, indicating successful convergence. Plots showing posterior distributions, trace plots and a posterior predictive check are provided in the Supporting Information (Figures  S5  and S6 ). We computed R 2 of the model using the "add_ic" function of the "brms" R package  (Bürkner, 2017). To evaluate spatial autocorrelation, we computed Moran's I for model residuals using geographical coordinates of locations. Observed and expected values did not differ significantly (p =. 83), indicating a lack of spatial autocorrelation among residuals.
| Implications for above-ground biomass estimates To quantify the magnitude of error introduced to estimations of AGB if tree palms are pooled with other trees, we first used with values where tree palm biomass was calculated using species-specific allometric equations developed specifically for palms that include information on plant height  (Feldpausch et al. , 2012; Marshall et al. , 2012). Unfortunately, allometric equations have not yet been developed for most palm species, and most groundbased forest inventory datasets do not include height measurements. As an alternative, we calculated tree palm AGB using the family-level allometric equation (based on diameter only) from the study by  Goodman et al. (2013). We then added this value of tree palm AGB to non-palm tree AGB calculated with the method of  Chave et al.
| Effect of size threshold on tree palm abundance To examine how the 10 cm d. b. h. threshold (commonly used by forest inventories) could affect conclusions about palm abundance patterns, we analysed separately Alwyn Gentry's transect data  (Phillips & Miller, 2002) , which includes all woody stems ≥2. 5 cm d. b. h. in 144 locations (0. 1 ha each) distributed globally throughout the (sub) tropical moist broadleaf biome (Supporting Information Figure  S2 ). We found a striking pattern across biogeographical realms in terms of tree palm occurrence, relative basal area and relative abundance (Figures  1b  and 2 ). The proportion of locations where tree palms were recorded was, by far, highest in the Neotropics (84%) compared with any other realm (41% in Australasia/Oceania, 13% in Afrotropics and 11% in IndoMalaya; Figure  2a ).
Also, non-human animal seed dispersers influence palm diversification and diversity via eco-evolutionary interactions  (Onstein et al. , 2017) , and these mutualisms could also potentially influence contemporary palm abundance patterns. For instance,  Onstein et al. (2018)  argued that palms with megafaunal fruits (some of which could also be large tree palms) may have reduced abundance and increased extinctions owing to the extinction of their megafaunal dispersers (also see  Doughty et al. , 2016). Quantifying these effects will require additional phylogenetic analyses, which will be attempted soon, once a comprehensive species-level phylogeny of the palm family is available. Finally, our dataset does not include swamp habitats, and some tree palms are known for forming monodominant stands, especially in swampy conditions associated with large carbon pools.
| Implications for above-ground biomass estimates One motivation for our study was to gain a better understanding of the potential error introduced to estimates of AGB when tree palms F I G U R E 7 Phylogeny of palms  (Faurby, Eiserhardt, Baker, & Svenning, 2016)  showing the presence (in bold colour) or absence (in faint colour) for each species in each biogeographical realm based on the world checklist of palms (WCSP, 2017) in the "level 3" geographical units defined by the International Working Group on Taxonomic Databases (TDWG;  Brummitt, 2001)  and biogeographical realms defined by  Olson et al. (2004). The outermost ring is black for species known to reach a maximal diameter at breast height (d. b. h. ) ≥ 10 cm ("tree palms" in this study), grey for species with maximal d. b. h. < 10 cm, and white for species for which maximum d.
b. h. information was not available (28%)  (Kissling et al. , 2019)  are pooled with other trees. Specifically, standard allometric equa- where BA palm was <5%, indicating that error from palms is probably negligible when palms are minor components of the total forest basal area. However, the difference between the two methods increases with BA palm , and the difference ranged from -15 to 6% in our dataset. Future analyses using data on stem height are needed to refine these estimates, and additional species-and region-specific palm allometric models will be important to improve AGB estimates in many Neotropical forests where tree palms are abundant. Fortunately, increasingly available remote sensing data on canopy height (e. g. , NASA GEDI) will improve this situation when paired with appropriate ground-based data.
| Palm life-forms and the effect of size threshold To some extent, the patterns we report reflect these differences in palm life diversity across biogeographical realms. For example, clades that assume a climbing form (e. g. , rattans in the subfamily Calamoideae) constitute a major component of palm diversity in the IndoMalayan and Australasian biogeographical realms  (Couvreur et al. , 2015). In the   Lutz et al. (2018)  showed that half the above-ground living biomass in a global dataset of forest plots was stored in the largest trees, which made up 1% of the total stems. On the other hand, major components of biodiversity are neglected by using a size threshold of 10 cm d. b. h. , because a substantial amount of tropical rain forest diversity exists as understorey and slender climbing plants  (Cicuzza et al.
, 2013; Hubau et al. , 2019). For palms, we estimate that c. 40% of all species reach a diameter of 10 cm  (Kissling et al. , 2019;  Figure  7 ). The plot dataset we analysed included a total of 126 palm species, which represent c. 5% of the total known palm species and nearly 20% of the species known to reach 10 cm d. b. h. Meanwhile, lowering the diameter cut-off for the Gentry transect dataset from 10 to 2. 5 cm resulted in the inclusion of 51 additional palm species despite containing < 25% the number of palm individuals. Our analysis of the Gentry transect data provides additional confirmation that biogeographical differences in palm life-form diversity can affect the perception of palm abundance. For example, small-diameter climbing palms are diverse and more common in IndoMalaya compared with arborescent palms  (Couvreur et al.
| Conclusions and future directions The results of our analysis of tree palm abundance show that tree palms are not only quintessentially tropical, but also overwhelmingly in South America and AfriTRON in Africa  (Hubau et al. , 2013; Lewis et al. , 2013 , Lopez-Gonzalez et al. , 2009; Lopez-Gonzalez et al. , 2011). Besides storage and organization of data to facilitate research, these networks strengthen the entire research chain (seeking field funding, fieldwork, developing protocols, allometries, collaborations, training, database design and quality control). Networks such as these are essential for conducting broad-scale and data-rich analyses. As development of these networks continues, it will be especially useful if they are even denser than today and even more balanced in terms of biomes, biogeographical realms and natural and disturbed sites.
5 times the interquartile range. Circles show the values for individual locations locations, tree palms (≥10 cm d. b. h. ) accounted for a total of 20,029 out of 661,194 individual stems (3%). The majority of tree palms (95%) belonged to 126 species and 71 genera (representing c. 5 and c. 39% of globally accepted palm species and genera, respectively). The remaining 5% of tree palm individuals in our dataset were identified to genus only. Across all locations, tree palms accounted for c. 1. 4% of the total basal area sampled.
(2014)  linked palm abundance in Amazonian forests with aspects of soil structure and suggested that edaphic properties, such as soil depth and texture, might affect plant composition and abundance indirectly by influencing stem turnover rates. Additionally, CWM wood density is geographically structured in our dataset; locations in western Amazonia tend to have lighter CWM wood density than locations in eastern Amazonia, F I G U R E 5 Palm relative basal area (BA palm , as a percentage, shown on a log 10 scale) versus the ratio of estimated total aboveground biomass (AGB) in each location calculated using the family-level (diameter-only) equation from Goodman et al. (2013) for palms and the equation from Chave et al. (2014) for trees (AGB Goodman+Chave ) versus total AGB when palms are treated as trees using the equations of Chave et al.
tions generated for trees that estimate AGB from stem diameter perform in a relative poor manner when estimating biomass for palms because, unlike other trees, growth in diameter and growth in height of palms are largely decoupled. The magnitude of error this introduces clearly depends, in part, on tree palm relative abundance. In our dataset, pooling tree palms with other trees added error to AGB estimates, but the direction of the error was not consistent. Pooling palms with dicots most often led to an overestimation of AGB [compared with estimates based on using the family-level model from the study by Goodman et al. (2013)  for palms and the dicot models of Chave et al. (2014)  for non-palm trees]. Variation in the magnitude and direction of these estimates emerged, in part, from the geographical variation in the equation of Chave et al.
We also depend on the centuries of work completed by palm taxonomists. W. L. E. 's contribution was supported by a research grant ( 00025354 ) from  VILLUM FONDEN. J. C. S. considers this work a contribution to his VILLUM Investigator project " Biodiversity Dynamics in a Changing World " funded by  VILLUM FONDEN  (grant  16549 ). R. M. was supported by  Vetenskapsrådet  ( 2019-03758 ). This work was supported by the  Danish Council for Independent Research Natural Sciences  (grant  4181-00158 ) to H. B,.
Bactris and Geonoma species in Amazonia; Dypsis in Madagascar;Pinanga and Licuala in IndoMalaya; Pritchardia in parts of Oceania). Neotropics, in contrast, many abundant palm species grow as large canopy trees (e. g. , Astrocaryum chambira, Euterpe precatoria, Iriartea deltoidea, Oenocarpus bataua), which are among the hyperdominant species of Amazonia (ter Steege et al. , 2013). Meanwhile, under- storey species also make up a large portion of palm diversity in the Neotropical, IndoMalayan and Australasian biogeographical realms (e. g. , (Bastin et al. , 2018;Lutz et al. , 2018;Slik et al. , 2013; patterns of abundance we report, because the forest inventory datasets we used only include trees with a large diameter (e. g. , 10 cm). On the one hand, plots censused with this size threshold are adequate for quantifying important forest properties(Bastin et al.
Non-alcoholic steatohepatitis (NASH) is a chronic liver disease. There is a clear need to develop pharmacological treatment for patients with NASH as well as biomarkers that can diagnose the disease. We describe a trial of semaglutide treatment for NASH, identify key patient characteristics and compare the relationship of patient characteristics and non-invasive biomarkers/scores. NCT02970942 is a randomised, double-blind, placebo-controlled, multi-national Phase 2 trial of daily subcutaneous semaglutide (0. 1 mg, 0. 2 mg, 0. 4 mg) in patients with biopsy-confirmed NASH, F1-F3 fibrosis, NAFLD Activity Score ≥ 4, and body mass index (BMI) > 25 kg/m 2. Exploratory analyses were performed to evaluate correlations between baseline parameters and biomarkers in NASH. Mean (standard deviation [SD]) age of 320 randomised patients was 55 (11) years, mean BMI was 36 (6) kg/m 2 , and 199 (62%) had type 2 diabetes.
With an increasing number of patients developing NASH-related end-stage liver disease, there is a need for effective pharmacological treatments, and a need for non-invasive biomarkers for those at high risk of liver-related complications. Here we describe the study design of a Phase 2 trial investigating semaglutide for the treatment of NASH (ClinicalTrials. gov identifier NCT02970942) and identify key characteristics of patients with NASH from analysis of the enrolled population. Moreover, we analysed how to potentially diagnose F2-F3 fibrosis by assessing correlations between the key patient characteristics and specific biomarkers, including FibroScan®, Fib-4 and ELF, with fibrosis stage.
ALT and/or AST are currently used in different diagnostic panels, such as the FibroTest  [43] , NashTest  [44] , NFS  [45] , FibroMeter™  [46] , Fib-4 index  [47]  and other different models  [48, 49]. As part of this trial, patients were stratified by presence of T2D. Overall, accuracy of Fib-4, ELF, AST and ALT for classifying fibrosis stage appeared to be fractionally higher for patients with T2D than those without T2D. Circulating MCP-1 levels, CK-18 fragments M30 and M65, BMI, HbA 1c and fasting plasma glucose showed poor accuracy in classifying fibrosis stage in patients with or without T2D. Overall, the diagnostic accuracy of baseline characteristics and biomarkers was not conclusive for the full population, patients with T2D or patients without T2D. In conclusion, the clinical profile of enrolled patients in the NCT02970942 study was typical for patients with NASH.
Data availability Deidentified participant data will be made available for this Article on a specialised SAS data platform. Datasets from Novo Nordisk will be available permanently after completion of the study and completion of data analysis. Access to data can be made through a request proposal form and the access criteria can be found online (novonordisk-trials. com). Data will be shared with bona fide researchers submitting a research proposal requesting access to data. Data use is subject to approval by the Independent Review Board.
Population Ethical approval (N°2018-031) was obtained from the institutional review board of Paris Diderot University Hospitals (IRB 00006477). From the JIS database, several inclusion criteria were defined for contacts:  (1)  all contacts between 2011 and 2015, (2) contacts considered to be relevant to the purpose of JIS, (3) contacts from gamblers and not from gamblers' relatives, and (4) contacts concerned gambling and not any other problem behavior. Between January 2011 and December 2015, the helpline received 83,858 contacts, of which 68,556 (81. 75%) were considered irrelevant, ie, errors or jokes. From the remaining 15,302 contacts related to the purpose of JIS, 4038 (26. 4%) contacts from gamblers' relatives were excluded, and we included 9474 (61. 9%) contacts who were gamblers (Figure  1 ).
Data Collection For each contact, the counselors systematically collected the following data: (1) medium used to contact JIS (helpline or real-time chat); (2) characteristics of the contacts: age and gender, gambler or relative, and first contact with the platform; (3) gambling types: bets (included gambling on sports and horse racing, without distinction), casino, scratch cards, poker, lottery, and other games; (4) media used for gambling (ie, solely offline, Web-based, or both); (5) proxies for the severity of problem gambling: gambling frequency (less than weekly vs weekly or more), time since gambling initiation, and history of suicide attempts; (6) contacts' attitudes to referral (request for referral or refusal to be referred);  (7)  actual referral (we considered that the contact led to an actual referral when he or she was encouraged to resume a follow-up that already started or to consult a known or a new address).
Missing Data Two variables-medium used and duration of the contact-were automatically collected and, therefore, presented no missing data. Age, media used for gambling, gambling frequency, and time since gambling initiation had high rates of missing data (32. 4%, 34. 0%, 50. 2%, and 59. 3%, respectively), while gender was collected in almost all cases (0. 3% missing data). Contact referral was systematically reported and presented no missing data. The main reason for missing data was when the counselor failed to ask the question, which can be considered missing at random (MAR)  [32]. Multiple imputation is a general approach to the problem of missing data, which aims to allow for the uncertainty generated by the missing data by creating several different plausible imputed datasets-using existing values from other variables-and appropriately combining results obtained from each of them.