Resultados de investigación

The expected log predictive density (elpd_loo) provides a measure of model fit, with higher values indicating better predictive performance. The effective number of parameters (p_loo) reflects model complexity, and the LOO Information Criterion (looic) facilitates comparison across models (lower is better). Standard errors quantify the uncertainty in these estimates., Peer reviewed

All versions are available at DOI 10.6084/m9.figshare.28229432, Dispersal is essential for all organisms to colonize new areas. Like many other aquatic invertebrates, Anostraca (Crustacea) disperse passively as dormant fertilized eggs. Here, we first study the genetic diversity of Branchinecta fairy shrimps (identified morphologically as B. readingi) inhabiting lakes in Saskatchewan and British Columbia, Canada. Second, we attempt to reconstruct the phylogeny and dispersal events that explain their genetic distribution. Finally, we compare genetic diversity and distribution of Branchinecta sp. and Artemia franciscana, the 2 anostracans inhabiting the Saskatchewan lakes but with different salinity niches. To this aim, we sequenced CO1 and 16S gene fragments of the Branchinecta individuals collected from 8 Saskatchewan lakes. We found a relatively high number of haplotypes for both markers (79 in CO1 and 76 in 16S) in Branchinecta from Saskatchewan. Three genetic clades at the CO1 gene region were found in Saskatchewan Branchinecta, one of which was widely recorded previously in the United States (but described as B. mackini). By contrast, 54 CO1 haplotypes were previously recorded in A. franciscana in Saskatchewan, with all CO1 sequences belonging to one genetic clade, suggesting that Branchinecta sequences might originate from several distinct Ice Age refugia, contrary to A. franciscana, which might originate from a single refuge. High density of suitable habitats (>100 000 lakes in Saskatchewan), weak priority effects, and frequent dispersal events could explain high genetic diversity found in the studied Branchinecta. However, we found no genetic support for a distinction between B. readingi and B. mackini., Funding was provided by Spanish National Plan projects CGL2010-16028 and [grant number CGL2016-76067-P] (AEI/FEDER, EU), and the PICS program funded by CSIC [grant number PIC2015FR4] in Spain and CNRS (PICS07360) in France. DL was supported by the Juan de la Cierva Formacion [grant number FJC2021-046991-I] awarded by the Spanish Ministry of Science and Innovation. AJG received funding from mobility grants of the Spanish Ministry of Science and Innovation [grant number PR2008-0293] and the Spanish Ministry of Education [grant number PR2010-0602]. Additional support was provided by the Canadian Wildlife Service. Ministerio de Ciencia e Innovación; Fundación General CSIC and Centre national de la recherche scientifique (CNRS); Agencia Estatal de Investigación; Ministerio de Educación y Formación Profesional., Peer reviewed

[Objective] This study aimed to evaluate clinical control in chronic obstructive pulmonary disease (COPD), the consequences in terms of treatment decisions, and their potentially associated factors during follow-up of patients in real-life clinical practice., [Methods] EPOCONSUL 2021 is a cross-sectional audit that evaluated the outpatient care provided to patients with a diagnosis of COPD in respiratory clinics in Spain and multivariable logistic regression models to assess the relationships between clinical control and clinical inertia., [Results] 4225 patients from 45 hospitals in Spain were audited. Clinical control was analyzed in 1804 (42.7%) patients who met all the Spanish COPD Guidelines (GesEPOC) criteria. 49.1% of patients were classified as uncontrolled, and 42.2% of patients disagreed with the level of control determined by their doctor, which was reported as good during the visit. There was therapeutic inertia (TI), in other words not making any change or taking any action in the treatment of COPD, in 68.4% of uncontrolled patients and no action was taken during the visit for 9.1% of uncontrolled patients. Factors associated with TI in uncontrolled patients were disagreement with the degree of control reported by the doctor who performed the examination ☯physician classifies and reports disease as controlled versus uncontrolled, OR: 3.37 (2.33–4.88), p<0.001] and having a lower burden of associated comorbidities ☯Charlson comorbidity index ≥3 versus <3, OR 0.8 (0.1–3.0), p = 0.014]. The probability of disagreeing with the physician’s classification of the degree of COPD control in uncontrolled patients was lower in patients with severe exacerbations ☯OR 0.3 (0.17–0.78), p = 0.009] and those with more exacerbations in the last year ☯OR 0.6 (0.4–0.9), p = 0.019]., [Conclusions] Therapeutic inertia exists in more than half of uncontrolled patients and is more likely when there is disagreement with the assessment of the physician responsible for the visit, who reported there being good disease control, a situation that was more likely in patients with less history of exacerbations., Peer reviewed

All versions are available at DOI 10.5281/zenodo.15683270, This is the dataset and script used for the research manuscript entitled "Climate warming drives the breakdown of plant-pollinator mutualisms despite phenological matching", Peer reviewed

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Centres and investigators participating in the EPOCONSUL 2021 study., Peer reviewed

All versions are available 10.5281/zenodo.15683270, This is the dataset and script used for the research manuscript entitled "Climate warming drives the breakdown of plant-pollinator mutualisms despite phenological synchrony", Peer reviewed

Peer reviewed

Accurate forecasting of patient arrivals at emergency departments (EDs) is vital for efficient resource allocation and high-quality patient care. In this study we investigate the relevance of exogenous variables, namely tourism, weather, calendar and demographic variables, in forecasting ED visits in the reference hospital in Palma de Mallorca, a city with significant seasonal population fluctuations due to tourism. Using a machine learning approach, we develop a model that predicts ED visits based solely on these exogenous variables. We test different machine learning algorithms (random forests, support vector machines, and feedforward neural networks) with different combinations of input variables and compare their symmetric mean average percentage errors (SMAPEs). Our findings reveal that calendar information, resident, and tourist population data are statistically significant for the accuracy of the predictions, while the addition of weather data does not provide any further improvement. Comparison of non-time-series with time-series prediction models reveals that the latter provide better accuracy for short prediction horizons (e.g., shorter than a week). Furthermore, time-series models become less or equally accurate to models relying only on exogenous variables for long prediction horizons (e.g., fortnight or month). Our study highlights the importance of carefully selecting predictive variables to ensure short- and long-term, robust and reliable forecasts. This demonstrates that, despite their lower complexity, non-time-series models with well-chosen input variables can be as effective as time-series models when predicting for long time horizons., Peer reviewed

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