Modelling bioremediation of contaminated effluents by Ulva ohnoi. - A predictive perspective

Ulva spp. are tolerant to salinity variations and exhibit easy acclimation, playing an essential role in the depollution of aquatic ecosystems precisely due to their high efficiency in absorbing and accumulating nutrients. For this reason, Ulva spp. becomes an attractive solution for recovering areas that suffer the impacts of problems such as the eutrophication of anthropogenic origin. In addition to being a promising alternative for the blue bioeconomy, it can contribute to the sustainability of economic activities in coastal areas. Therefore, the present study aimed to develop and elucidate the behavior of Ulva ohnoi using predictive surface response models. The algae were grown under different concentrations of nutrient and salinity levels, as predicted by the experimental design, and it was evaluated according to the potential of the biomass to absorb the nutrients, as well as its photosynthetic performance and biochemical parameters. Our study confirmed the high efficiency and preference of Ulva ohnoi in the absorption of nitrogen dissolved in the medium in the form of NH4+ and that salinity is an essential factor in the dynamics and speed of ammonium absorption. The absorption of orthophosphate by U. ohnoi is reverted to the culture medium when subjected to long-term cultivation. This process was more intense because of low salinity, even at conditions of availability of the compound. The 3D-models of response surfaces elucidate the behavior of Ulva ohnoi, attributing a correlation between nutrient availability and salinity and the biological behavior of the species. In view of what is exposed by these models, as well as the effects of saline distribution along the Lagoon, the following regions of the lagoon are suggested: Center-North, Center and South - as potential areas for the implementation of bioremediation projects with Ulva ohnoi.

A designed experiment for CdS-AgBr photocatalyst toward methylene blue

A boosted photocatalytic activity was observed for the CdS-AgBr nanocomposite in the degradation of methylene blue (MB). The experimental design method based on the response surface methodology (RSM) approach used to study the simultaneous interaction effects between the influencing variables. Analysis of variance (ANOVA) of the results confirmed a significant model for processing the data because an F value of 32.34 for the suggested model was higher than that of the critical value of F_{0.05, 14, 13} = 2.55 at 95% confidence interval. This analysis also showed a non-significant lack of fit (LOF) (as a measure of the randomness of the deviations around the obtained data) because the LOF F value of 8.27 was smaller than that of the critical value of F_{0.05, 10, 3} = 8.79. R² values near to unity were achieved (the multiple correlation coefficients R² (R² = 0.9627), adjusted R² (adj-R² = 0.9226), and predicted R² (pred-R² = 0.7423)). Six center points suggested by the model included the following conditions: pH, 6.1; C_MB, 3.5 mg/L; a dose of the catalyst, 0.68 g/L; and irradiation time, 40.5 min. During the center point runs, the degradation efficiencies were obtained in the range of 38 to 43%. The optimal run included pH, 9; catalyst dosage, 1 g/L; irradiation time, 60 min; and C_MB, 2 mg/L, and the best removal efficiency of 98% was achieved during these conditions.

Development and optimization of a new tioconazole vaginal mucoadhesive film using an experimental design strategy. Physicochemical and biological characterization

A new tioconazole (TCZ) mucoadhesive film, based on a biodegradable chitosan/ hydroxypropyl methylcellulose (CH/HPMC) blend, was developed for treatment of vaginal candidiasis. The formulation was optimized through an I-optimal design (minimizing the integral of the prediction variance across the factor space), where the impact of the proportion of the ingredients and processing variables on the quality of the final product was evaluated. Both, the thickness of the film and the swelling index, which affect patients' comfort and compliance, were considered. Mechanical testing, such as load at break, elongation at break, and mucoadhesive strength were also included as dependent variables. The optimal mucoadhesive film formulation, which should be obtained at a drying temperature of 30 °C, was found to include the combination of CH and HPMC (forming polymers) at 0.25:0.75 ratio, a mixture of polyethylene glycol 400 and propylene glycol as plasticizers (0.07:0.93, 5% w/w), and TCZ loaded at 15 % w/w. The optimal preparation was subjected to exhaustive characterization studies, which revealed that the drug was entrapped in the polymeric matrix in an amorphous state and that the film exhibited a smooth and uniform surface, demonstrating excellent component compatibility. In vitro tests showed that the formulation has an excellent time to kill value (3 min) and lacks cytotoxicity, suggesting that it should be highly effective and safe.

Trial timing and pattern-information analyses of fMRI data

Pattern-information approaches to fMRI data analysis are becoming increasingly popular but few studies to date have investigated experimental design optimization for these analyses. Here, we tested several designs that varied in the number of trials and trial timing within fixed duration scans while participants encoded images of animals and tools. Trial timing conditions with fixed onset-to-onset timing ranged from slow 12-s trials with two repetitions of each item to quick 6-s trials with four repetitions per item. We also tested a jittered version of the quick design with 4-8s trials. We assessed the effect of trial timing on three dependent measures: category-level (animals vs. tools) decoding accuracy using a multivoxel pattern analysis, item-level (e.g., cat vs. dog vs. lion) information estimates using pattern similarity analysis, and memory effects comparing pattern similarity scores across repetitions of individual items subsequently remembered vs. forgotten. For single trial estimates, category decoding was equal across all trial timing conditions while item-level information and memory effects were better detected using slow trial timing. When modeling events on an item-by-item basis across all repetitions of a given item, a larger number of quick, regularly spaced trials provided an advantage over fewer slow trials for category decoding while item-level information was comparable across conditions. Jittered and non-jittered versions of the quick trial timing did not differ significantly in any analysis. These results will help inform experimental design choices in future studies planning to employ pattern-information analyses and demonstrate that design optimization guidelines developed for univariate analyses of a few conditions are not necessarily optimal for pattern-information analyses and condition-rich designs.