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Optimization of passivation layers for corrosion protection of silicon-based microelectrode arrays
(2000)
Bacillus subtilis and Bacillus licheniformis are widely used for the large-scale industrial production of proteins. These strains can efficiently secrete proteins into the culture medium using the general secretion (Sec) pathway. A characteristic feature of all secreted proteins is their N-terminal signal peptides, which are recognized by the secretion machinery. Here, we have studied the production of an industrially important secreted protease, namely, subtilisin BPN′ from Bacillus amyloliquefaciens. One hundred seventy-three signal peptides originating from B. subtilis and 220 signal peptides from the B. licheniformis type strain were fused to this secretion target and expressed in B. subtilis, and the resulting library was analyzed by high-throughput screening for extracellular proteolytic activity. We have identified a number of signal peptides originating from both organisms which produced significantly increased yield of the secreted protease. Interestingly, we observed that levels of extracellular protease were improved not only in B. subtilis, which was used as the screening host, but also in two different B. licheniformis strains. To date, it is impossible to predict which signal peptide will result in better secretion and thus an improved yield of a given extracellular target protein. Our data show that screening a library consisting of homologous and heterologous signal peptides fused to a target protein can identify more-effective signal peptides, resulting in improved protein export not only in the original screening host but also in different production strains.
Optimization of the immobilization of bacterial spores on glass substrates with organosilanes
(2016)
Spores can be immobilized on biosensors to function as sensitive recognition elements. However, the immobilization can affect the sensitivity and reproducibility of the sensor signal. In this work, three different immobilization strategies with organosilanes were optimized and characterized to immobilize Bacillus atrophaeus spores on glass substrates. Five different silanization parameters were investigated: nature of the solvent, concentration of the silane, silanization time, curing process, and silanization temperature. The resulting silane layers were resistant to a buffer solution (e.g., Ringer solution) with a polysorbate (e.g., Tween®80) and sonication.
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).