Biodiversity of anuran endoparasites from a transitional area between the Atlantic Forest and Cerrado biomes in Brazil: new records and remarks.

The richness of metazoan endoparasites associated with 25 anuran species from a transitional area between Cerrado and Atlantic Rain Forest from Brazil is here presented. We present and discuss the type-host and localities, the current taxonomic status, remarks on morphological features, biological cycle, and new records of these parasites. Nine hundred and seventy-eight anurans of four families (Bufonidae, Hylidae, Microhylidae, and Leptodactylidae) were collected during four expeditions in a private forested area from Northwest of São Paulo state. The richness of metazoan parasites was composed of nematodes (21), acanthocephalans (2), digeneans (18), monogeneans (1), cestodes (1), and oligochaetes (1), resulting in 44 taxa that are presented with their respective prevalence and the range of abundance in host populations. Among these parasites, nine were found in immature stages which usually complete their life cycle in fishes, snakes, birds or mammals, attesting the trophic relation of amphibians and their parasites within the regional vertebrate community. We reported 23 anuran species as new hosts resulting in 79 new associations between anurans and parasites. Gorgoderina diaster and Bursotrema tetracotyloides are reported for the first time in Brazil, and our finding of Clinostomum cf. complanatum represents the first in South American anurans.

Prediction of Lignin Content in Different Parts of Sugarcane Using Near-Infrared Spectroscopy (NIR), Ordered Predictors Selection (OPS), and Partial Least Squares (PLS).

The building of multivariate calibration models using near-infrared spectroscopy (NIR) and partial least squares (PLS) to estimate the lignin content in different parts of sugarcane genotypes is presented. Laboratory analyses were performed to determine the lignin content using the Klason method. The independent variables were obtained from different materials: dry bagasse, bagasse-with-juice, leaf, and stalk. The NIR spectra in the range of 10 000-4000 cm-1 were obtained directly for each material. The models were built using PLS regression, and different algorithms for variable selection were tested and compared: iPLS, biPLS, genetic algorithm (GA), and the ordered predictors selection method (OPS). The best models were obtained by feature selection with the OPS algorithm. The values of the root mean square error prediction (RMSEP), correlation of prediction ( RP), and ratio of performance to deviation (RPD) were, respectively, for dry bagasse equal to 0.85, 0.97, and 2.87; for bagasse-with-juice equal to 0.65, 0.94, and 2.77; for leaf equal to 0.58, 0.96, and 2.56; for the middle stalk equal to 0.61, 0.95, and 3.24; and for the top stalk equal to 0.58, 0.96, and 2.34. The OPS algorithm selected fewer variables, with greater predictive capacity. All the models are reliable, with high accuracy for predicting lignin in sugarcane, and significantly reduce the time to perform the analysis, the cost and the chemical reagent consumption, thus optimizing the entire process. In general, the future application of these models will have a positive impact on the biofuels industry, where there is a need for rapid decision-making regarding clone production and genetic breeding program.