The biological mechanisms of butanol tolerance and the application of solvent-tolerant bacteria for environmental protection

Toxicity caused by the accumulation of butanol in fermentation media is an important factor limiting the concentration of butanol. There is currently no systematic research in place investigating the butanol tolerance mechanism of bacteria such as Clostridium acetobutylicum, which adapts to butanol stress and regulates its growth and metabolism. Here, research results related to the butanol tolerance of C. acetobutylicum are reviewed to understand the molecular basis of changes in butanol-tolerant strains. Organic solvent-tolerant bacteria play an important role in the fields of biofuel production, enzyme preparation and bioremediation. An analysis of limitations of the application of organic solvent-tolerant bacteria has revealed that future research should focus on combining the microbial tolerance phenotype with specific utilization to achieve an optimal balance between organic solvent tolerance and production. This review serves as a reference for the improvement and engineering of strains that tolerate organic solvents. © 2019 Society of Chemical Industry     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.     

基于自适应免疫遗传算法的IPPS问题研究

针对工艺规划与调度集成(Integration of Process Planning and Scheduling,IPPS)的多目标优化问题,将遗传算法基本理论与免疫应答机理相结合,并引入IPPS问题研究中,构造了融遗传算法和免疫机理为一体的自适应免疫遗传算法(Self-adaptive Immune Genetic Algorithm,SIGA)。该算法以遗传算法为主体并加入免疫算子,通过免疫系统的自适应、免疫识别、学习、记忆、激增和抑制等复杂的信息处理机理,有效地解决了规模大、约束多、目标函数非单一的IPPS问题。最后以实例进行仿真优化,验证了模型算法的正确性和有效性。     

Induction of Oxidative Stress and Antioxidative Mechanisms in Arabidopsis thaliana after Uranium Exposure at pH 7.5

To evaluate the environmental impact of uranium (U) contamination, it is important to investigate the effects of U at ecologically relevant conditions. Since U speciation, and hence its toxicity, strongly depends on environmental pH, the present study aimed to investigate dose-dependent effects of U at pH 7.5. Arabidopsis thaliana plants (Mouse-ear Cress) were exposed for three days to different U concentrations at pH 7.5. In the roots, the increased capacities of ascorbate peroxidase and glutathione reductase indicate an important role for the ascorbate-glutathione cycle during U-induced stress. However, a significant decrease in the ascorbate redox state was observed after exposure to 75 and 100 µM U, indicating that those roots are severely stressed. In accordance with the roots, the ascorbate-glutathione cycle plays an important role in the antioxidative defence systems in A. thaliana leaves exposed to U at pH 7.5 as the ascorbate and glutathione biosynthesis were upregulated. In addition, small inductions of enzymes of the antioxidative defence system were observed at lower U concentrations to counteract the U-induced stress. However, at higher U concentrations it seems that the antioxidative defence system of the leaves collapses as reductions in enzyme activities and gene expression levels were observed.     

Trans-Splicing Improvement by the Combined Application of Antisense Strategies

Spliceosome-mediated RNA trans-splicing has become an emergent tool for the repair of mutated pre-mRNAs in the treatment of genetic diseases. RNA trans-splicing molecules (RTMs) are designed to induce a specific trans-splicing reaction via a binding domain for a respective target pre-mRNA region. A previously established reporter-based screening system allows us to analyze the impact of various factors on the RTM trans-splicing efficiency in vitro. Using this system, we are further able to investigate the potential of antisense RNAs (AS RNAs), presuming to improve the trans-splicing efficiency of a selected RTM, specific for intron 102 of COL7A1. Mutations in the COL7A1 gene underlie the dystrophic subtype of the skin blistering disease epidermolysis bullosa (DEB). We have shown that co-transfections of the RTM and a selected AS RNA, interfering with competitive splicing elements on a COL7A1-minigene (COL7A1-MG), lead to a significant increase of the RNA trans-splicing efficiency. Thereby, accurate trans-splicing between the RTM and the COL7A1-MG is represented by the restoration of full-length green fluorescent protein GFP on mRNA and protein level. This mechanism can be crucial for the improvement of an RTM-mediated correction, especially in cases where a high trans-splicing efficiency is required.     

Towards sensible toxicity testing for nanomaterials: proposal for the specification of test design

During the last decade, nanomaterials (NM) were extensively tested for potential harmful effects towards humans and environmental organisms. However, a sound hazard assessment was so far hampered by uncertainties and a low comparability of test results. The reason for the low comparability is a high variation in the (1) type of NM tested with regard to raw material, size and shape and (2) procedures before and during the toxicity testing. This calls for tailored, nanomaterial-specific protocols. Here, a structured approach is proposed, intended to lead to test protocols not only tailored to specific types of nanomaterials, but also to respective test system for toxicity testing. There are existing standards on single procedures involving nanomaterials, however, not all relevant procedures are covered by standards. Hence, our approach offers a detailed way of weighting several plausible alternatives for e.g. sample preparation, in order to decide on the procedure most meaningful for a specific nanomaterial and toxicity test. A framework of several decision trees (DT) and flow charts to support testing of NM is proposed as a basis for further refinement and in-depth elaboration. DT and flow charts were drafted for (1) general procedure—physicochemical characterisation, (2) choice of test media, (3) decision on test scenario and application of NM to liquid media, (4) application of NM to the gas phase, (5) application of NM to soil and sediments, (6) dose metrics, (S1) definition of a nanomaterial, and (S2) dissolution. The applicability of the proposed approach was surveyed by using experimental data retrieved from studies on nanoscale CuO. This survey demonstrated the DT and flow charts to be a convenient tool to systematically decide upon test procedures and processes, and hence pose an important step towards harmonisation of NM testing.     

Multi-objective genetic algorithm for berth allocation problem considering daytime preference

Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed.     

Digital Microfluidics for Manipulation and Analysis of a Single Cell

The basic structural and functional unit of a living organism is a single cell. To understand the variability and to improve the biomedical requirement of a single cell, its analysis has become a key technique in biological and biomedical research. With a physical boundary of microchannels and microstructures, single cells are efficiently captured and analyzed, whereas electric forces sort and position single cells. Various microfluidic techniques have been exploited to manipulate single cells through hydrodynamic and electric forces. Digital microfluidics (DMF), the manipulation of individual droplets holding minute reagents and cells of interest by electric forces, has received more attention recently. Because of ease of fabrication, compactness and prospective automation, DMF has become a powerful approach for biological application. We review recent developments of various microfluidic chips for analysis of a single cell and for efficient genetic screening. In addition, perspectives to develop analysis of single cells based on DMF and emerging functionality with high throughput are discussed.     

Sensitivity Analysis on Ladder Network Equivalent Circuit Parameters of Power Transformer

In this article, novel methods and ideas are introduced, which altogether lead to an accurate model of power transformer windings. First, by the determination of non-dominant (hidden) resonances from frequency-response tests, a ladder model is proposed. Next, it is improved by assigning different values for similar elements of each section of the model. The parameters are obtained by minimizing the error function via a genetic algorithm. Sensitivity analysis is then applied to the obtained model to achieve further examinations and tests. Measurements have been driven from the windings of a 20/0.4-kV, 1600-kVA transformer. Modeling, methodologies, and sensitivity analysis in this article can be very useful for future research aiming to find internal faults of the transformer with the frequency response analysis.