No abstract

Environmental conservation and regeneration actions must consider plant species that add value by restoring structural and functional aspects of the ecosystem. The forest species, Peltophorum dubium, has several characteristics that make its use viable in projects of restoration of degraded areas, making it necessary to study the species' responses to different cultivation environments. Thus, 500 seeds were selected and sown in vermicompost substrates prepared with different proportions of cattle rumen content (CR) and sugarcane bagasse (SB) (V1 - 60% CR x 40% SB / V2 - 50% CR x 50 % SB / V3 - 40% CR x 60% SB / V4 - 70% CR x 30% SB) and in an commercial substrate (Plantmax®). At 79 days after sowing, 20 seedlings per treatment were removed; they were weighed and measured, resulting in growth, dry matter, and vigor data. The vermicompost substrates were sent to the chemical analysis laboratory to determine their chemical composition. The data were submitted to normality analysis. Analysis of variance was performed for normal data and the Kruskal Wallis test for data that did not show normality. The means or medians were compared using the Tukey test at a 5% probability level. Based on the results related to the growth and vigor of P. dubium seedlings, it is possible to state that the productive potential of the species was higher in the vermicompost substrates.

Branchoneta is an option as live food for aquaculture and it is necessary to develop more studies to get information that makes possible its culture on a large scale. Our aim was to establish a method that results in a higher percentage hatching of D. brasiliensis. We analyzed if the cyst density could cause any significant difference in hatching; for that test we use 2 different conditions: (I) 15 ml glass tube (T1, T2, and T3) with 25 (twenty-five) cysts/repetition; and (II)  Erlenmeyer of 150 ml (T4, T5, and T6) with 25 cysts/repetition totaling 75 cysts/treatment, with triplicates to all treatments. We also tested 3 different pH conditions: acid (pH 3), neutral (pH~7/distilled water) and alkaline (pH 8), all in natural light and temperature. We conclude that there is no difference between the treatments, for none of the conditions tested. But other results have to be considered as Hatching Speed index and the Average Hatching Time with best results for the treatments T3, T1, and T2, respectively. The density of 0.6 ml/cyst (glass tube) resulted in faster hatching, which shows the necessity of further studies to analyze the speed of hatching under different conditions of density. The relative frequency showed that the hatchings peak occurs in the second day. We conclude that pH and density, in this study, not influenced the beginning of the hatching process.

The Dendrocephalus brasiliensis, a native species from South America, is a freshwater crustacean well explored in conservational and productive activities. Its main characteristics are its rusticity and resistance cysts production, in which the hatching requires a period of dehydration. Independent of the species utilization nature, it is essential to manipulate its cysts, such as the counting using microscopes. Manually counting is a difficult task, prone to errors, and that also very time-consuming. In this paper, we propose an automatized approach for the detection and counting of Dendrocephalus brasiliensis cysts from images captured by a digital microscope. For this purpose, we built the DBrasiliensis dataset, a repository with 246 images containing 5141 cysts of Dendrocephalus brasiliensis. Then, we trained two state-of-the-art object detection methods, YOLOv3 (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks), on DBrasiliensis dataset in order to compare them under both cyst detection and counting tasks. Experiments showed evidence that YOLOv3 is superior to Faster R-CNN, achieving an accuracy rate of 83,74%, R2 of 0.88, RMSE (Root Mean Square Error) of 3.49, and MAE (Mean Absolute Error) of 2.24 on cyst detection and counting. Moreover, we showed that is possible to infer the number of cysts of a substrate, with known weight, by performing the automated counting of some of its samples. In conclusion, the proposed approach using YOLOv3 is adequate to detect and count Dendrocephalus brasiliensis cysts. The DBrasiliensis dataset can be accessed at: https://doi.org/10.6084/m9.figshare.13073240.