Mass spectrometry characterisation of fatty acids from metabolically engineered soybean seeds

Improving the quality and performance of soybean oil as biodiesel depends on the chemical composition of its fatty acids and requires an increase in monounsaturated acids and a reduction in polyunsaturated acids. Despite its current use as a source of biofuel, soybean oil contains an average of 25 % oleic acid and 13 % palmitic acid, which negatively impacts its oxidative stability and freezing point, causing a high rate of nitrogen oxide emission. Gas chromatography and ion mobility mass spectrometry were conducted on soybean fatty acids from metabolically engineered seed extracts to determine the nature of the structural oleic and palmitic acids. The soybean genes FAD2-1 and FatB were placed under the control of the 35SCaMV constitutive promoter, introduced to soybean embryonic axes by particle bombardment and down-regulated using RNA interference technology. Results indicate that the metabolically engineered plants exhibited a significant increase in oleic acid (up to 94.58 %) and a reduction in palmitic acid (to <3 %) in their seed oil content. No structural differences were observed between the fatty acids of the transgenic and non-transgenic oil extracts.

Development of a Urea Bioprobe Based on Platinized Boron‐Doped Diamond Electrodes

Urea (CH₆ON₂) is one of the main human nitrogen‐based metabolic wastes. The concentration of urea in blood lies between 2.5–7 mM for healthy individuals, and is commonly used as an indicator for several diseases that may alter this value. Spectrophotometric methods are employed for the determination of blood urea concentration during clinical assays. Although these methods are sensitive, they make use of toxic reagents and complex reaction schemes. Therefore, in this research we present the bioelectrochemical determination of urea by the use of the protein urease (E.C.3.1.1.5) along with a nano‐platinized boron‐doped diamond electrode. This approach has been proven to be efficient and sensitive providing a platform with detection limits of 1.79 mM (S/N=3). The linear range resulted from 1 mM to 25 mM for the determination of urea, and response time of five minutes.     

Directional persistence and the optimality of run-and-tumble chemotaxis

E. coli does chemotaxis by performing a biased random walk composed of alternating periods of swimming (runs) and reorientations (tumbles). Tumbles are typically modelled as complete directional randomisations but it is known that in wild type E. coli, successive run directions are actually weakly correlated, with a mean directional difference of 63°. We recently presented a model of the evolution of chemotactic swimming strategies in bacteria which is able to quantitatively reproduce the emergence of this correlation. The agreement between model and experiments suggests that directional persistence may serve some function, a hypothesis supported by the results of an earlier model. Here we investigate the effect of persistence on chemotactic efficiency, using a spatial Monte Carlo model of bacterial swimming in a gradient, combined with simulations of natural selection based on chemotactic efficiency. A direct search of the parameter space reveals two attractant gradient regimes, (a) a low-gradient regime, in which efficiency is unaffected by directional persistence and (b) a high-gradient regime, in which persistence can improve chemotactic efficiency. The value of the persistence parameter that maximises this effect corresponds very closely with the value observed experimentally. This result is matched by independent simulations of the evolution of directional memory in a population of model bacteria, which also predict the emergence of persistence in high-gradient conditions. The relationship between optimality and persistence in different environments may reflect a universal property of random-walk foraging algorithms, which must strike a compromise between two competing aims: exploration and exploitation. We also present a new graphical way to generally illustrate the evolution of a particular trait in a population, in terms of variations in an evolvable parameter.     

Microhardness of a dental restorative composite resin containing nanoparticles polymerized with argon ion laser

The objective of this study was to compare the microhardness of two resin composites (microhybrid and nanoparticles). Light activation was performed with argon ion laser 1.56 J (L) and halogen light 2.6 J (H) was used as control. Measurements were taken on the irradiated surfaces and those opposite them, at thicknesses of 1, 2 and 3 mm. To evaluate the quality of polymerization, the percentages of maximum hardness were calculated (PMH). For statistical analysis the ANOVA and Tukey tests were used (p ≤ 0.05). To microhybrid was shown that the hardness with laser was inferior to the hardness achieved with halogen light, for both the 1 mm and 2 mm. The nanoparticles polymerized with laser, presented lower hardness even on the irradiated surface, than the same surface light activated with halogen light. The microhybrid attained a minimum PMH of 80% up to the thickness of 2 mm with halogen light, and with laser, only up to 1 mm. The nanoparticles attained a minimum PMH of 80% up to 3 mm thickness with halogen light and with laser this minimum was not obtained at any thickness. Based on these results, it could be concluded that light activation with argon ion laser is contra-indicated for the studied nanoparticles.