Heterologous Expression of Cyanobacterial Cyanase Gene (CYN) in Microalga Chlamydomonas reinhardtii for Bioremediation of Cyanide Pollution.

Recombinant DNA technology offered the creation of new combinations of DNA segments that are not found together in nature. The present study aimed to produce an ecofriendly bioremediation model to remediate cyanide pollution from a polluted marine system. Cyanide is a known toxic compound produced through natural and anthropogenic activities. An Agrobacterium-tumefaciens-mediated genetic transformation technique was used to generate transformed Chlamydomonas reinhardtii using plant expression vector pTRA-K-cTp carries isolated coding sequence of the cyanobacterial cyanase gene (CYN) isolated from Synechococcus elongatus (PCC6803). qRT-PCR analysis showed the overexpression of CYN in transgenic C. reinhardtii, as compared with the respective wild type. Growth parameters and biochemical analyses were performed under cyanide stress conditions using transgenic and wild C. reinhardtii for evaluating the effect of the presence of the cyanobacterial cyanase gene in algae. The transgenic C. reinhardtii strain (TC. reinhardtii-2) showed promising results for cyanide bioremediation in polluted water samples. Cyanide depletion assays and algal growth showed a significant resistance in the transgenic type against cyanide stress, as compared to the wild type. Genetically modified alga showed the ability to phytoremediate a high level of potassium cyanide (up to150 mg/L), as compared to the wild type. The presence of the CYN gene has induced a protection response in TC. Reinhardtii-2, which was shown in the results of growth parameter analyses. Therefore, the present study affirms that transgenic C. reinhardtii by the CYN coding gene is a potential effective ecofriendly bioremediator model for the remediation of cyanide pollutants in fresh water.

Control of COVID-19 system using a novel nonlinear robust control algorithm.

COVID-19 has been a worldwide concern since the outbreak. Many strategies have been involved such as suppression and mitigation strategies to deal with this epidemic. In this paper, a new mathematical-engineering strategy is introduced in order to control the COVID-19 epidemic. Thereby, control theory is involved in controlling the unstable epidemic alongside with the other suggested strategies until the vaccine will hopefully be invented as soon as possible. A new robust control algorithm is introduced to compensate the COVID-19 nonlinear system by propose a proper controller after using necessary assumptions and analysis are made. In addition, the Variable Transformation Technique (VTT) is used to simplify the COVID-19 system. Furthermore, the Most Valuable Player Algorithm (MVPA) is applied in order to optimize the parameters of the proposed controller. The simulation results are based on the daily reports of two cities Hubei (China) and Lazio (Italy) since the outbreak. It can be concluded that the proposed control algorithm can effectively compensate the COVID-19 system. In addition, it can be considered as an effective mathematical-engineering strategy to control this epidemic alongside with the other strategies.

Biotransformation of Natural Saponins / 中国实验方剂学杂志

Saponin is a kind of complex compounds with triterpenoid or spiral aglycones.Natural saponins are used as substrates,and many novel compounds are obtained by biotransformation technology. Especially, converted products of saponins with strong activities provide valuable lead compounds for the research and development of new drugs. Saponins can be divided into triterpenoid saponin and steroidal saponin according to the structure of the mother nucleus. There are about 89 reported saponin components,including 56 triterpenoid saponins and 33 steroidal saponins. Biological enzyme catalysis,microbial transformation and intestinal microflora transformation are the main bioconversion technologies and key development directions of saponins. The research and optimization technology of biological enzyme and microbial transformation of saponins are the effective methods to prepare active secondary saponins. The biotransformation reaction of saponins mainly includes hydrolysis,redox and rearrangement,resulting in the formation of aglycones,secondary glycosides and their derivatives. The hydrolysis of saponin sugar chains was the main biological transformation pathway, and could generate a number of secondary saponins with less glycosyl groups. The secondary saponins could be absorbed into blood and become real active ingredients in body. Preparation of rare secondary saponins,discovery of lead compounds and development of new drugs are the main directions of biotransformation of saponins. The studies on the metabolism and mechanism of natural saponins by microbial and intestinal microbial biotransformation will also become hotspots. According to relevant papers at home and abroad,the researches on transformation technique,transformation approach and transformation reaction of saponins from natural products in the past thirty years were summarized, and the prospects of research and development were also analyzed to provide scientific basis for further study and comprehensive utilization of these conversion products.

Improvement of protein binding sites prediction by selecting amino acid residues' features.

One of the main focuses of bioinformatics community is the study of the relationship between the structure of the protein molecules and their functions. In the literature, there are various methods that consider different protein-derived information for predicting protein functions. In our research, we focus on predicting the protein binding sites, which could be used to functionally annotate the protein structures. In this paper we consider a set of sixteen amino acid residues' features, and by applying various feature selection techniques we estimate their significance. Although the number of features in our case is not high, we perform feature selection in order to improve the prediction power and time complexity of the prediction models. The results show that by applying proper feature selection technique, the predictive performance of the classification algorithms is improved, i.e., by considering the most relevant features we induce more accurate models than if we consider the entire set of features. Furthermore, the model complexity, as well as the training and testing times are decreased by performing feature selection. We also compare our approach with several existing methods for protein binding sites prediction. The results demonstrate that the existing methods considered in this research are specific and applicable to the group of proteins for which the model was developed, while our approach is more generic and can be applied to a wider class of proteins.

Multi-choice stochastic transportation problem involving general form of distributions.

Many authors have presented studies of multi-choice stochastic transportation problem (MCSTP) where availability and demand parameters follow a particular probability distribution (such as exponential, weibull, cauchy or extreme value). In this paper an MCSTP is considered where availability and demand parameters follow general form of distribution and a generalized equivalent deterministic model (GMCSTP) of MCSTP is obtained. It is also shown that all previous models obtained by different authors can be deduced with the help of GMCSTP. MCSTP with pareto, power function or burr-XII distributions are also considered and equivalent deterministic models are obtained. To illustrate the proposed model two numerical examples are presented and solved using LINGO 13.0 software package.