β-Glucosidase was purified from Brassica oleracea by salting out with ammonium sulfate and hydrophobic interaction chromatography. Results demonstrated that the enzyme is a dimer (130 kD) made up of one major (80 kD) and one minor subunit (50 kD). The pH optimum is 6.0, with 50% of the enzyme's original activity remaining between pH 4.0 and pH 7.0. The temperature optimum is 35C, and activity did not decrease after two hours of exposure to this temperature. The activity of the enzyme was investigated on four substrates, 4-Nitrophenyl β-D-glucopyranoside (p-NPG), ortho-Nitrophenyl-β-D-glucopyranoside (o-NPG), para-Nitrophenyl-β-D-galactoside (p-NPGal) and ortho-Nitrophenyl-β-D-galactoside (o-NPGal), and km values were shown to be 0.755 mM, 0.174 mM, 0.988 mM and 0.213 mM, while Vmax values were 604 U/mg, 38 U/mg, 556 U/mg and 308 U/mg, respectively. The enzyme is completely inhibited by gluconolactone and glucose against p-NPG as substrate, with ki values of 0.038 mM and 0.64 mM, respectively. To our knowledge, this is the first study demonstrating purification and characterization of β-glucosidase from broccoli, thus providing a better understanding of its role in the plant, and establishing a basis for further research. Practical Applications To our knowledge, this is the first study demonstrating purification and characterization of β-glucosidase from broccoli, thus providing a better understanding of its role in the plant, and establishing a basis for further research. The results of this research highlight the potential of the enzyme isolated from broccoli for further research. Succeeding efforts would involve optimization of this procedure for increasing the enzyme yield, in order to make it a viable candidate for industrial application.

The relationship between single nucleotide polymorphisms (SNPs) and phenotypes is noisy and cryptic due to the abundance of genetic factors and the influence of environmental factors on complex traits, which makes the idea of applying artificial neural networks (ANNs) as universal approximates of complex functions promising. In this study, we compared different ANN architectures and input parameters to predict the adult length of Pacific lampreys, which is the primary indicator of their total migratory distance. Feedforward and simple recurrent network architectures with a different range of input parameters and different sizes of hidden layers were compared. Results indicate that the highest performing ANN had an accuracy of 67.5% in discriminating between long and short specimens. Sensitivity and specificity were 62.16% and 70.73%, respectively. Our results imply that feedforward ANN architecture with a single hidden neuron is enough to solve the problem of specimen classification. Nonetheless, while ANNs are useful at approximating functions with unknown relationships in the case of SNP data, additional work needs to be performed to ensure that the chosen SNP markers are related to functional regions related to the examined trait, as the use of non-specific markers will result in the introduction of noise into the dataset.