Stocking densities and feeding strategies in shrimp and tilapia polyculture in tanks

The objective of this work was to evaluate the performance of Pacific marine shrimp (Litopenaeus vannamei) and tilapia (Oreochromis niloticus), in a polyculture in tanks subjected to different stocking densities and feeding strategies, in comparison with monoculture. Two experiments were performed, at the same time, in a completely randomized design with three treatments and four replicates each. Treatments for experiment I were: monoculture with 10 shrimp per m² (10S:0T); polyculture with 10 shrimp and 0.5 tilapia per m² (10S:0.5T); and polyculture with 10 shrimp and 1 tilapia per m² (10S:1T). Shrimp was the main crop, and feed was provided based on shrimp biomass. Treatments for experiment II were: monoculture with 2 tilapia per m² (2T:0S); polyculture with 2 tilapia and 2.5 shrimp per m² (2T:2.5S); and polyculture with 2 tilapia and 5 shrimp per m² (2T:5S). Tilapia was the main crop, and feed was provided based on fish requirements. In the experiment I, tilapia introduction to shrimp culture resulted in lower shrimp growth and poor feed conversion rate. In experiment II, shrimp introduction to tilapia culture did not interfere with fish performance. Polyculture is more efficient with the combination of 2 tilapia and 2.5 or 5 shrimp per m² and feed based on fish requirements.

Assessment of the Correlation Between Insertion Torque and Resonance Frequency Analysis of Implants Placed in Bone Tissue of Different Densities

The primary stability of dental implants is fundamental for osseointegration. Therefore, this study aimed to assess the correlation between insertion torque (IT) and resonance frequency analysis (RFA) of implants placed in mandibles and maxillas of different bone densities. Eighty dental implants were placed in maxillas and mandibles, and IT and the implant stability quotient (ISQ) were measured at the time of implant insertion. Bone density was assessed subjectively by the Lekholm and Zarb index. The type I and II densities were grouped together (group A)as were the type III and IV densities (group B). The IT in group A was higher (Student t test, P = .0013) than in group B (46.27 +/- 18.51 Ncm, 33.62 +/- 14.74 Ncm, respectively). The implants placed in group A showed higher ISQ (Student t test, P = .0004) than those placed in group B (70.09 +/- 7.50, 63.66 +/- 8.00, respectively). A significant correlation between IT and the ISQ value was observed for group A (Pearson correlation test; r = 0.35; P = .0213) and for group B (r = 0.37; P = .0224). Within the limitations of this study, it was possible to conclude that there is a correlation between IT and RFA of implants placed in mandibles and maxillas of different bone densities.

Comparison of changes in dental and bone radiographic densities in the presence of different soft-tissue simulators using pixel intensity and digital subtraction analyses

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Stocking densities of juvenile Brycon orthotaenia: production parameters and economic benefits in net cages

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Densities and antimicrobial resistance of Escherichia coli isolated from marine waters and beach sands

Bacterial resistance is a rising problem all over the world. Many studies have showed that beach sands can contain higher concentration of microorganisms and represent a risk to public health. This paper aims to evaluate the densities and resistance to antimicrobials of Escherichia coli strains, isolated from seawater and samples. The hypothesis is that microorganisms show higher densities in contaminated beach sands and more antimicrobial resistance than the water column. Density, distribution, and antimicrobial resistance of bacteria E. coli were evaluate in seawater and sands from two recreational beaches with different levels of pollution. At the beach with higher degree of pollution (Gonzaguinha), water samples presented the highest densities of E. coli; however, higher frequency of resistant strains was observe in wet sand (71.9 %). Resistance to a larger number of antimicrobial groups was observe in water (betalactamics, aminoglycosides, macrolides, rifampicins, and tetracyclines) and sand (betagalactamics and aminoglycosids). In water samples, highest frequencies of resistance were obtain against ampicilin (22.5 %), streptomycin (15.0 %), and rifampicin (15.0 %), while in sand, the highest frequencies were observe in relation to ampicilin (36.25 %) and streptomycin (23.52 %). At the less polluted beach, Ilha Porchat, highest densities of E. coli and higher frequency of resistance were obtain in wet and dry sand (53.7 and 53.8 %, respectively) compared to water (50 %). Antimicrobial resistance in strains isolated from water and sand only occurred against betalactamics (ampicilin and amoxicilin plus clavulanic acid). The frequency and variability of bacterial resistance to antimicrobials in marine recreational waters and sands were related to the degree of fecal contamination in this environment. These results show that water and sands from beaches with a high index of fecal contamination of human origin may be potential sources of contamination by pathogens and contribute to the dissemination of bacterial resistance.

GIS-based analytical tools for transport planning: spatial regression models for transportation demand forecast

Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.