Application of soft computing techniques to estimate the scouring depth formed by crossing jets.

The scouring depth caused by the water jet outputs from a dam is one of the crucial parameters for design purposes. Due to the importance of the subject, several laboratory studies have been conducted to understand this subject. Nevertheless, using soft computing techniques is a new attitude for modeling and predicting the natural process parameters. Herein, the types of soft computing techniques for estimating the scouring depth of a plunge pool caused by the symmetrical crossing jets have been explored. The parameters involved in the scouring phenomenon are densimetric Froude number, tailwater depth, vertical jet angle, horizontal crossing angles, and the distance between the crossing points of two jets and the water level. The prediction results show that the Multi-Layer Perceptron (MLP) model gives the best performance among the other models tested here. The Pearson correlation coefficient, root mean square error, and normalized root mean square error for the MLP model were 0.9527, 0.9039, and 19.36% for the test phase, respectively. Furthermore, based on the sensitivity analysis, the parameters, for instance, tailwater depth and vertical jet angle have the highest and lowest effects for predicting the scouring depth of a plunge pool, respectively.

Towards effective offloading mechanisms in fog computing.

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

Optimization of in vitro refolding conditions of recombinant Lepidium draba peroxidase using design of experiments.

The main objective of this study was to optimize the in vitro refolding conditions of the recombinant Lepidium draba peroxidase (LDP). Initially, the effects of various factors were investigated on LDP refolding yield using one-factor-at-a-time (OFAT) method. Based on the OFAT results, optimum concentrations for LDP refolding were 2 M urea, 2 mM CaCl2, 0.42 mM l-glutathione oxidized (GSSG), 0.20 mg/ml protein, and 12 µM hemin as well as pH 7. Secondly, according to the OFAT results, design of experiments (DOE) was applied for investigation of the interactions between factors including protein (P), urea (U), CaCl2 (C), and GSSG (G). The results showed the possible interaction between PC, PG, PU, and GU. Lastly, response surface methodology (RSM) was used for final refolding conditions optimization. The final optimized refolding conditions for LDP were conducted as 2 M urea, 1 mM CaCl2, 0.70 mM GSSG, 0.07 mM DTT, 0.15 mg/ml protein which they obtained from RSM results and 12 µM hemin, and pH 7 according to the results of OFAT method. Overall, under optimal conditions, 23.4 mg active refolded LDP per liter of expression medium was obtained. So, the refolding yield was calculated to be approximately 48%.

Heterologous Expression, Purification and Characterization of a Peroxidase Isolated from Lepidium draba.

Peroxidase is one of the most widely used enzymes in biotechnology and medicine. In the current study, cDNA encoding peroxidase from Lepidium draba (LDP) was cloned and expressed in Escherichia coli BL21 (DE3) cells in the form of inclusion bodies (IBs). To achieve purified active enzyme, IBs were solubilized before being purified and refolded. The deduced amino acid sequence (308) of the LDP gene (924 bp) revealed 88.96% identity to horseradish peroxidase C1A (HRP C1A). The results of basic local alignment search tool (BLAST) and phylogenetic analysis of the protein sequence showed that this enzyme belongs to the neutral group of class III plant peroxidases. According to sequence analysis and structural modeling, critical amino acids in heme and calcium binding domain as well as cysteine residues were conserved as HRP C1A except for calcium binding domain where valine228 was replaced with isoleucine. The far-UV circular dichroism (CD) results were confirmed by homology modeling data showing the enzyme consists mainly of α-helices as other plant peroxidases. Overall, according to the results of catalytic activity and refolding yield, LDP can be introduced as a novel peroxidase for medical and biotechnology applications.

In silico locating the immune-reactive segments of Lepidium draba peroxidase and designing a less immune-reactive enzyme derivative.

Peroxidases have broad applications in industry, environmental as well as pharmaceutical and diagnosis. Recently applicability of peroxidases in cancer therapy was mentioned. In the present study, a horseradish peroxidase homologue from Lepidium draba was subjected to in silico analyzes aiming at identifying and locating immune-reactive regions. A derivative sequence with decreased immunogenicity and increased stability also suggested. The tertiary structure of the enzyme was predicted. The functional and structural importance of residues was annotated as well as the conservatory status of each residue. The immune-dominant regions of protein were predicted with various software. N-terminal 4 residues, NFSHTGL (186-192), PRNGN (210-214), PLVRAYADGTQKFFN (261-275), and last 4 residues in C-terminal were predicted to be the consensus immunogenic segments of L. draba peroxidase. The modifications were applied to wild type sequence in order to mitigate its immune-reactiveness. The modifications were based on predicted energetic status of residues and naturally occurred amino acids in each position of the enzyme sequence, extracted from alignment file of 150 homologous peroxidases. The new enzyme derivative is predicted to be less immune-reactive and more stable. Thus the sequence is better suited to therapeutic applications.