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Records of the cave-dwelling bats (Mammalia: Chiroptera) of Hispaniola with an examination of seasonal variation in diversity

100%

Nunez-Novas M. S. , Leon Y. M. , Mateo J. , Davalos L. M.

Acta Chiropterologica

2016

tom 18

nr 1

Despite a long history of scientific collection of bats, Hispaniola remains the least studied island of the Greater Antilles. Using standardized trapping methods during the wet and dry season at four major caves — Honda de Julián, La Chepa, Los Patos, and Pomier #4, we sampled a total of 1,472 individuals in four families, 11 genera and 12 species (of 18 recorded for the island). We report significantly fewer captures on the second night of sampling. We document seasonal variation in abundance of Macrotus waterhousii, Monophyllus redmani, and Artibeus jamaicensis, that results in 1–4 more species captured in the wet season. Additionally, singleton captures at all caves except for Honda de Julian produced wide confidence intervals in estimates of richness. Finally, we highlight the role of caves as major ecosystems for maintaining Hispaniolan mammal biodiversity. The high diversity recorded at La Chepa, together with possible declines at the historically very diverse Los Patos, highlight the conservation importance of all surveyed caves.

Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis

88%

Mateo J. , Rius-Peris J. M. , Maraña-Pérez A. I. , Valiente-Armero A. , Torres A. M.

Biocybernetics and Biomedical Engineering

2021

tom Vol. 41, no. 2

792--801

Acute bronchiolitis is the most common lower respiratory tract infection of infancy. About 2% of infants under 12 months of age hospitalized with this condition each epidemic season. The choice of the correct treatment is important for the evolution of the disease. Therefore, a prediction model for medical treatment identification based on extreme gradient boosting (XGB) machine learning (ML) method is proposed in this paper. Four supervised machine learning algorithms including a k-nearest neighbours (KNN), decision tree (DT), Gaussian Naı¨ve Bayes (GNB) and support vector machine (SVM) were compared with the proposed XGB method. The performance of these methods was then tested implementing a standard 10-fold cross-validation process. The results indicate that the XGB has the best prediction accuracy (94%), high precision (>0.94) and high recall (>0.94). The KNN, SVM, and DT approaches also present moderate prediction accuracy (>87), moderate specificity (>0.87) and moderate sensitivity (>0.87). The GNB algorithm show relatively low classification performance. Based on these results for classification performance and prediction accuracy, the XGB is a solid candidate for a correct classification of patients to be treated. These findings suggest that XGB systems trained with clinical data may serve as a new tool to assist in the treatment of patients with acute bronchiolitis.