Reduction in lignin content and increase in the antioxidant capacity of corn and sugarcane silages treated with an enzymatic complex produced by white rot fungus.

The objective was to evaluate the effect of the addition of 0, 10, 20, and 30 mg.kg-1 of natural matter of a lignocellulosic enzymatic complex produced by the white rot fungus on the chemical composition, cumulative gas production in vitro, and antioxidant compounds of corn and sugarcane silages. After being chopped and treated with the enzymatic complex, the plants were packed in vacuum-sealed bags. After 60 days, the mini silos were opened and the samples were dried in a forced ventilation oven at 55 °C for analysis of the proposed parameters. The experiment was conducted in a completely randomized design with four replicates per treatment. In the corn silage, there was a linear reduction in the lignin concentration. In the sugarcane silage showed a reduction of 12% in the lignin concentration, a linear reduction in the hemicellulose content, and a decrease of 8% in the cellulose concentration compared to the control treatment. The lignin monomers had linear increases in the syringyl:guaiacil ratio. This reflected on significant increases in the concentration of the non-fibrous carbohydrates and the A + B1 fraction of the carbohydrates, and a reduction in the C fraction. The in vitro gas production increased, the time of colonization and initiation of in vitro fermentation linearly decreased in both silages. The phenolic compounds and the antioxidant capacity increased linearly with the addition of the enzymes in both silages. The addition of the lignocellulolytic enzymes to the silages caused changes in the cell wall, resulting in improvements in the in vitro fermentative parameters, besides the additional effect on the antioxidant capacity. There was an effect of the addition of the enzymes on the evaluated fodder, and the best concentration was, on average, 20 mg kg-1 MN for corn silage and 10 mg kg-1 NM for sugarcane silage.

Multidimensional and fuzzy sample entropy (SampEnMF) for quantifying H&E histological images of colorectal cancer.

In this study, we propose to use a method based on the combination of sample entropy with multiscale and multidimensional approaches, along with a fuzzy function. The model was applied to quantify and classify H&E histological images of colorectal cancer. The multiscale approach was defined by analysing windows of different sizes and variations in tolerance for determining pattern similarity. The multidimensional strategy was performed by considering each pixel in the colour image as an n-dimensional vector, which was analysed from the Minkowski distance. The fuzzy strategy was a Gaussian function used to verify the pertinence of the distances between windows. The result was a method capable of computing similarities between pixels contained in windows of various sizes, as well as the information present in the colour channels. The power of quantification was tested in a public colorectal image dataset, which was composed of both benign and malignant classes. The results were given as inputs for classifiers of different categories and analysed by applying the k-fold cross-validation and holdout methods. The derived performances indicate that the proposed association was capable of distinguishing the benign and malignant groups, with values that surpassed those results obtained with important techniques available in the Literature. The best performance was an AUC value of 0.983, an important result, mainly when we consider the difficulties of clinical practice for the diagnosis of the colorectal cancer.