dataesr/scientific-paragraphs-categorization huggingface dataset:
The highest density intervals would be affected by the skewness of the probabilities  (Hyndman, 1996). This skewness disappears when the sampled probabilities are transformed back to quantile. The ratios of observations falling within their respective 90% credible interval, or below and above, indicate the validity of the imputation's uncertainty obtained by the D-vine. A perfect model would have 90% of observations inside their respective credible intervals. The highest density intervals are computed with the hdrcde R package  (Hyndman et al. , 2021). The validity of the model is further assessed with the Nash-Sutcliffe efficiency (NSE) score, given by: NSE = 1 - ∑ 𝑇 𝑡=1 (𝑋 𝑖𝑚𝑝 𝑡 -𝑋 𝑜𝑏𝑠 𝑡 ) 2 ∑ 𝑇 𝑡=1 (𝑋 𝑜𝑏𝑠 𝑡 -𝑋 𝑜𝑏𝑠 ) 2 , ( 19 ) where  is the total number of timesteps of the observed values,    is the mean of the values generated by MCMC for the date ,    is the observed value for the date  and   is the mean of the observed values  (Knoben et al.
Data and case study The imputation method is applied to a dataset of skew surge time series for nine tide gauges located along the French Atlantic coast (Fig. 2 ). The skew surge is defined as the difference between the maximal observed sea level and the highest predicted astronomical high tide for a single tidal cycle, which may occur at different times in that cycle  (Saint Criq et al. , 2022) , resulting in an approximately 12 h and 25 min timestep. Extreme events are of interest for this variable as they can contribute to coastal flooding. Data availability varies by station, with measurements starting during the 1970s for most. The method is applied to a period of 46 years, from 1971 to 2016. This dataset is characterized by numerous missing values, ranging from 4. 4% to 61. 8% of the time series depending on the station (Fig.
2 ). These missing values are sometimes isolated but at other times extend over long periods, spanning several years in the worst cases. This dataset was already used in the previous studies of  Hamdi et al. (2019)  and  Andreevsky et al. (2020)  (albeit with a different selection of neighbor stations). The skew surge data is assumed to be MCAR. This simplifying assumption is made to follow the methodology of  Hasler et al. (2018)  and to avoid having to model the missingness mechanism. As a result, the probability of missingness is assumed independent of the value of the skew surge. For the skew surge this assumption may not always be valid, as an extreme sea level or tidal event could increase the chance of measurement device failure. Nonetheless, the methodology is tested with this assumption to assess the performance offered by the D-vine.
Among the tide gauge dataset spanning the French Atlantic coast, the target station of La Rochelle is chosen to test the model. This station has 42. 4% missing values over the 46 years of the dataset (Fig. 2 ). Eight other neighbor stations for La Rochelle are selected using the method presented in  Hamdi et al. (2019)  and  Andreevsky et al. (2020). This method selects neighboring stations according to their ratio of common extreme events with the target station. The threshold for this ratio, above which a station is included as a neighboring station, is chosen to be sufficiently low so that there is enough regional information to impute each missing date from the target station. However, some stations with too few observations were not included in the D-vine to keep its dimensionality low enough for computational reasons, or because including these stations reduced the number of dates for which a valid subset can be obtained to compute the D-vine likelihood.
Introduction Connected health technology is playing an increasingly important role in healthcare. In this paper, we refer to connected health as the use of information technology, digital networks, artificial intelligence (AI) and machine learning (ML) to collect, share, and analyze data on individuals' health. Connected health technology is used to help patients, healthcare professionals, and health authorities make better-informed healthcare decisions and improve health outcomes. It differs from digital health in that it describes a system "where devices, services or interventions are designed around the patient's needs"  (1) , however digital health can be seen as a key subset of connected health activities. The power, complexity, functionality, and accessibility of connected health technologies are increasing rapidly, showing promise for improved and more equitable healthcare outcomes.
DISCUSSION This study assessed the PK parameters and safety of the FDC of rosuvastatin and ezetimibe compared with their individual formulations in Chinese healthy subjects. The point estimates of formulation ratios with 90% CIs for rosuvastatin, unconjugated ezetimibe, and total ezetimibe were all within 0. 80-1. 25 and thus confirmed the bioequivalence of the FDC to the individual formulations. The mean concentration-time profile was also similar for rosuvastatin, unconjugated ezetimibe, and total ezetimibe. Previously published studies on rosuvastatin/ ezetimibe FDC have established the benefit of FDC over the individual formulations. A phase III study, I-ROSETTE (NCT02749994), stated that an FDC significantly improved the lipid profiles when compared to rosuvastatin monotherapy (92. 3% vs 79.
In the present paper, we use a mathematical model mimicking the preference of world countries to trade in USD or CNY. This currency preference model is applied on the world trade network (WTN) built from the 2010-2020 UN Comtrade data  [2]. Our analysis, based purely on trade relations without taking account of external political factors or other considerations, clearly shows that, during this decade, a USDto-CNY transition took place with the result that the main part of the world countries would now prefer to trade in CNY instead of USD. The WTN is constructed from the UN Comtrade database  [2]  which provides the money matrix M encoding the transactions of all the commodities between all the countries. The money matrix element M c ′ c gives the total amount of commodities, expressed in USD of a given year, exported from the country c to the country c ′.
The UN Comtrade database concerns 194 countries for the period 2010-2020. The Markov chain of trade transactions is characterized by two WTN trade matrices S and S * whose elements are S c ′ c = M c ′ c /M * c and S * c ′ c = M cc ′ /M c. Here, M * c = c ′ M c ′ c (M c = c ′ M cc ′ ) gives the total amount of commodities exported from (imported to) the country c to (from) the rest of the world. Consequently, the matrix element S c ′ c (S * c ′ c ) gives the relative weight of the imports to (exports from) the country c ′ from (to) the country c. These matrix elements can be considered as link weights of a directed network (see e. g. [11] ). By construction, the sum of each column of the matrices S and S * is equal to 1, i. e. , c ′ S c ′ c = 1 and c ′ S * c ′ c = 1, ensuring the fact that the stochastic processes of the trade transactions, both in export and import directions, belong to the class of Markov chains.
ACKNOWLEDGMENTS We thank  L. Ermann  for his help to collect data from the  UN Comtrade database. We thank the  UN Statistics Division  to grant us a friendly access to the  UN Comtrade database. This research has been partially supported through the grant  NANOX N • ANR-17-EURE-0009  (project  MTDINA ) in the frame of the  Programme des Investissements d'Avenir, France. This research has been also supported by the  Programme Investissements d'Avenir   ANR-15-IDEX-0003.
As countries progress towards universal health coverage (UHC), they frequently develop explicit packages of health services compatible with UHC goals. As part of the Disease Control Initiative 3 Country Translation project, a systematic survey instrument was developed and used to review the experience of five low-income and lower-middle-income countries-Afghanistan, Ethiopia, Pakistan, Somalia and Sudan-in estimating the cost of their proposed packages. The paper highlights the main results of the survey, providing information about how costing exercises were conducted and used and what country teams perceived to be the main challenges. Key messages are identified to facilitate similar exercises and improve their usefulness. Critical challenges to be addressed include inconsistent application of costing methods, measurement errors and data reliability issues, the lack of adequate capacity building, and the lack of integration between costing and budgeting.
BMJ Global Health information on package characteristics and package development process in the six countries. All countries are part of an extended knowledge network regrouping LLMICs that had developed or are developing an EPHS, at least partially inspired by evidence from the third edition of the Disease Control Priorities (DCP3). 4 5 ] [8] [9]  Evidence for this paper was gathered using a systematic survey aimed at technical costing teams. The survey questionnaire is presented in online supplemental annex 1. It includes 28 factual and 10 normative questions on the choice of methodology, capacity, data issues and the use of costing estimates for policy and planning decisions. Instructions were provided with guidance on who should be involved in responses (one person identified as primary contact, but participation of the entire costing team encouraged), how responses would be used (countries named only on factual questions and respondents never identified by name) and terminology.
Data sources used in costing Different types of data were used in costing depending on costing methodology: quantity and price data for bottom-up methods; expenditure data for top-down approaches; and health and population data to evaluate target populations. Four out of five countries reported on the data sources. In all countries, national government sources were most used-predominantly data routinely collected by ministries of health-but they had to be largely complemented by additional sources, including primary data collection in two countries (online supplemental annex 2, tables Q18 and Q19). The variety of complementary sources listed in most countries for quantity and price data, and the fact that some countries had to resort to time-consuming primary data collection and/or searches through local cost studies and reports show that data collection was a challenge, particularly when using bottom-up methods.
Acknowledgements We are extremely grateful to the following persons and their teams for their effort and time invested in responding to our extensive survey: (in alphabetical order)  Karl Blanchet ,  Getachew Teshome Eregata ,  Alemayehu Hailu ,  Mohamed Jama ,  Reza Majdzadeh ,  Jacque Mallender ,  Mohamed Musa ,  Ahmad Salehi , and  Raza Zaidi. Special mention goes to  Manuel Carballo ,  Hassan Haghparast-Bidgoli ,  Maryam Huda , and  Gerard Abou Jaoude  for their careful review of the survey instrument,  Gavin Yamey  for his excellent support in editing the draft paper, and  Ina Gudumac  for coordination and editorial support. Finally, we wish to thank two anonymous referees for their careful and insightful reviews.
2. Using datasets on the biodiversity impacts and monetary costs (damage and management costs) of alien birds, we aimed to establish whether species with the most severe biodiversity impacts also had the highest costs; whether types of biodiversity impact were associated with high costs; and whether specific factors associated with alien species are linked to both damaging biodiversity impacts and high costs. 3. We identified a positive relationship between a specific type of biodiversity impact (predation) and costs, possibly because predation by alien birds can be severely damaging to native species and therefore attracts management actions. However, predation impacts are likely to occur more frequently and to be easier to identify than some other impact mechanisms such as hybridisation and transmission of diseases, and they are therefore likely to be more frequently managed and hence to have costs.
Two frameworks and a database have recently been developed which aim to improve understanding of the biodiversity and socio-economic impacts of alien species, by enabling impact data to be categorised, quantified and standardised in a manner that facilitates direct comparisons of impacts by their severity and type. These are the Environmental Impact Classification for Alien Taxa (EICAT)  (Blackburn et al. , 2014) , the Socio-economic Impact Classification for Alien Taxa (SEICAT)  (Bacher et al. , 2018)  and the InvaCost database  (Diagne, Leroy, Gozlan, Vaissière, Assailly, et al. , 2020). Under EICAT, the environmental impacts of alien species are categorised by their type using 12 impact mechanisms (e. g. competition with native species and predation of native species) and by their severity using five impact categories  (Minimal Concern [MC] , Minor  [MN] , Moderate  [MO] , Major  [MR]  and Massive  [MV] ).
Published guidelines (IUCN, 2020) provide a series of semi-quantitative impact scenarios which guide the assessment process. SEICAT uses data on the socio-economic impacts of alien species to assess how these impacts affect the well-being of people. Developed in tandem with EICAT, it adopts the same five impact categories (MC-MV). InvaCost is a living, publicly available database that provides a systematic, standardised methodology for the collection and treatment of data on the economic costs of alien species, enabling historical cost data in different currencies to be transformed to current, standardised values  (Diagne, Leroy, Gozlan, Vaissière, Nuninger, et al. , 2020). EICAT has been used to undertake assessments of the biodiversity impacts of alien species from a range of taxonomic groups such as Acacia species  (Jansen & Kumschick, 2022) , rabbits and hares  (Allmert et al.
, n. d. ) , and to demonstrate that inaction to manage the impacts of alien species can result in spiralling costs  (Ahmed et al. , 2022). It has also been used to describe and analyse costs occurring in specific regions of the world (studies on costs have been published for 16 countries and 8 regions to date) (InvaCost, 2022), and to demonstrate that the global costs associated with alien species are grossly underestimated  (Diagne et al. , 2021). Thus, EICAT, SEICAT and InvaCost provide useful datasets on alien species impacts. However, they each consider specific types of impact (biodiversity, human well-being and economic cost, respectively), so when used individually they do not provide information on the complete range of impacts caused by alien species. Combining data from EICAT, SEICAT and InvaCost may provide a 5.
K E Y W O R D S alien species, avian ecology, biological invasions, EICAT, InvaCost, invasive species, ornithology, predation, SEICAT, wildlife management more comprehensive understanding of these impacts. Indeed, identifying the complete range of biodiversity and socio-economic impacts caused by an alien species may better inform the prioritisation of management actions to mitigate its impacts. Combining data from EICAT, SEICAT and InvaCost may also improve our knowledge of the links between biodiversity impacts and economic costs. Identifying statistical links may enable the prediction of biodiversity impacts or economic costs for species lacking data on either. Furthermore, as biodiversity and human well-being are linked through the provision of ecosystem services  (Díaz et al. , 2019) , identifying connections between biodiversity and socio-economic impacts may inform a more holistic approach to the management of alien species.
g. the number of different habitats a species occupies (habitat breadth) or the number of different dietary items a species consumes (diet breadth) are consistently correlated with both more severe biodiversity impacts and socio-economic impacts, whilst traits associated with resource use (e. g. body mass and flockforming species) are less conclusive  (Evans et al. , 2014; Kumschick et al. , 2013; Shirley & Kark, 2009). They also indicate that the influence of some traits has been tested for biodiversity impacts  (Evans, Kumschick, et al. , 2018)  but not for socio-economic impacts, including for example, several traits associated with the distribution of alien birds, such as alien range size (Table  1 ). Here, we aim to use EICAT, SEICAT and InvaCost data to identify specific types of impacts and factors associated with alien birds that link their biodiversity impacts with their economic costs at the global scale.
| Data on biodiversity impacts Data on biodiversity impacts were taken from published global EICAT assessments  (Evans et al. , 2016)  and additional unpublished assessments completed in 2021, which were undertaken in accordance with the EICAT guidelines (IUCN, 2020). From these EICAT assessments, we took each alien bird species' most severe impact score (MC-MV) and the mechanism associated with this impact  (competition, predation, hybridisation etc. ). For some of these species, no impact data were available to assess their impacts (i. e. no data describing their impacts were found during online literature reviews completed for the EICAT assessments). These species were categorised as being 'data deficient' (DD) under EICAT. In total, our dataset included 121 alien bird species with an EICAT score (MC-MV) and 296 species that were DD (417 species in total) (see Table  S1 , Supporting Information).
| Data on economic costs The total costs caused by each alien bird species were calculated by summing each available economic cost record for an alien bird species from the InvaCost database (v4. 1). These records include costs associated with damage (e. g. to agriculture) and management (e. g. the eradication of an alien bird population due to its damaging biodiversity impacts). All costs have been converted in the database to US$ (2017 value). For complete information on InvaCost methodology and calculation methods, see  Leroy et al. (2022). There are many 'costs' associated with both the biodiversity and socio-economic impacts of alien species that are difficult to monetise (e. g. the extinction of a native bird species caused by a predatory alien bird species on an island, which results in a direct decline in biodiversity and may affect local communities by causing negative perceptions of their surrounding environment).
Thus, our analysis is of monetary costs (and hereafter, costs are referred to as 'monetary costs'). We then reviewed a global alien bird SEICAT assessment  (Evans et al. , 2020)  to gather any additional information on monetary costs caused by alien birds that were not in the InvaCost database. SEICAT assessments incorporate a literature review for each alien species to identify data describing its socio-economic impacts, including cost data (where available). Thus, we did not use the actual published SEICAT impact scores (MC-MV) for each alien bird species but rather reviewed the literature gathered on socio-economic impacts during the SEICAT assessments to identify any data on costs. The monetary costs of alien bird species with weak socioeconomic impacts may not have been calculated or reported because they are likely to be low (and therefore perceived not to be an issue of concern).
We treated all alien bird species that were categorised as DD under SEICAT (i. e. species for which no data on socio-economic impacts were identified during SEICAT assessments) as being DD under InvaCost (i. e. species for which no data on monetary costs were available). In total, our dataset included 40 species with cost data (either an actual cost (  22 species) or a $0 cost value (18 species)) and 378 species that were DD for costs (total = 417 species; the same 417 species assessed under EICAT) (see Table  S1 , Figure  S1 , Supporting Information). Our cost calculations included costs associated with the control or eradication of alien bird species, even if these actions were undertaken to manage biodiversity impacts. We recognise that there is a direct link between biodiversity impact and monetary cost in these cases.
Nevertheless, some alien bird species may have negligible biodiversity impacts that do not warrant control or eradication. Further, some alien species may be easier (and less costly) to control than others (e. g. flightless alien bird species such as the weka, or bird species with small alien populations). Therefore, control and eradication costs are likely to vary depending on the characteristics of the targeted alien bird species and the severity of its biodiversity impacts. Several of the 22 species with costs had either damage or management costs, but not both. This meant that the dataset of species was too small to undertake separate analysis of damage costs and management costs. We therefore combined damage and management costs for each species for the analysis. Given the small number of alien bird species in our dataset, we acknowledge that the cost data we identified is unlikely to capture the complete range of monetary costs caused by alien birds.
| Analysis-Linking severe biodiversity impacts with monetary costs; linking types of biodiversity impact with monetary costs Our analysis of links between biodiversity impacts and monetary costs was restricted to species for which we had both an EICAT score and a cost value (40 species We first compared the association between the severity of biodiversity impacts and monetary costs (phylogenetic linear regression using the phylolm package [and the phylolm function]) (Tung Ho & Ané, 2014), with biodiversity impact as a two-level predictor variable as previously described ('less severe' impacts and 'more severe' impacts), and costs as a continuous response variable). We then compared the association between different types of biodiversity impacts (using EICAT impact mechanism categories, e. g.
(2018) , enabled direct comparisons with the monetary cost analysis undertaken in this study. We analysed each response variable separately (univariate analysis) and then all variables together, to identify variables with the strongest influence on biodiversity impact and monetary cost (multivariate analysis). We checked for multi-collinearity among variables using the car package  (Fox & Weisberg, 2019) , finding slight evidence for this (highest VIF value = 3. 8; Table  S4 , Supporting Information). To address this, we removed two variables that were not found to be significant in univariate analysis (V2: Sociality and V10: Relative brain size). This reduced VIF values (all >3; Table  S4 , Supporting Information). During multivariate analysis, for the analysis of monetary costs (using the phylolm function), we used the dredge function in the MuMIn package  (Bartoń, 2020)  to undertake automated model simplification to identify the best-reduced model as ranked by AIC.
For the analysis of biodiversity impacts (using the phyloglm function), we were unable to use the dredge function. So we reduced the model manually; after each run of the model, we removed the least significant variable, repeating this process to find the best model (as measured by AIC). This approach to model simplification follows that adopted for a related study which identified factors which influence the severity of the biodiversity impacts of alien birds  (Evans, Kumschick, et al. , 2018). Data for monetary cost, native and alien range size, alien residence time, body mass, brain size, diet preference (animal matter) and diet preference (seeds and fruit) were.