Chronic wounds afford a hostile environment of damaged tissues that allow bacterial proliferation and further wound colonization. Escherichia coli is among the most common colonizers of infected wounds and it is a prolific biofilm former. Living in biofilm communities, cells are protected, become more difficult to control and eradicate, and less susceptible to antibiotic therapy. This work presents insights into the proceedings triggering E. coli biofilm control with phage, honey, and their combination, achieved through standard antimicrobial activity assays, zeta potential and flow cytometry studies and further visual insights sought by scanning electron microscopy and transmission electron microscopy. Two Portuguese honeys (PF2 and U3) with different floral origin and an E. coli-specific phage (EC3a), possessing depolymerase activity, were tested against 24- and 48-h-old biofilms. Synergic and additive effects were perceived in some phage–honey experiments. Combined therapy prompted similar phenomena in biofilm cells, visualized by electron microscopy, as the individual treatments. Honey caused minor membrane perturbations to complete collapse and consequent discharge of cytoplasmic content, and phage completely destroyed cells leaving only vesicle-like structures and debris. Our experiments show that the addition of phage to low honey concentrations is advantageous, and that even fourfold diluted honey combined with phage, presents no loss of antibacterial activity toward E. coli. Portuguese honeys possess excellent antibiofilm activity and may be potential alternative therapeutic agents in biofilm-related wound infection. Furthermore, to our knowledge this is the first study that assessed the impacts of phage–honey combinations in bacterial cells. The synergistic effect obtained was shown to be promising, since the antiviral effect of honey limits the emergence of phage resistant phenotypes.

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This work studied the influence of effluent recirculation upon the kinetics of anaerobic degradation of dairy wastewater in the feedless phase of intermittent upflow anaerobic sludge bed (UASB) reactors. Several laboratory-scale tests were performed with different organic loads in closed circuit UASB reactors inoculated with adapted flocculent sludge. The data obtained were used for determination of specific substrate removal rates and specific methane production rates, and adjusted to kinetic models. A high initial substrate removal was observed in all tests due to adsorption of organic matter onto the anaerobic biomass which was not accompanied by biological substrate degradation as measured by methane production. Initial methane production rate was about 45% of initial soluble and colloidal substrate removal rate. This discrepancy between methane production rate and substrate removal rate was observed mainly on the first day of all experiments and was attenuated on the second day, suggesting that the feedless period of intermittent UASB reactors treating dairy wastewater should be longer than one day. Effluent recirculation expressively raised the rate of removal of soluble and colloidal substrate and methane productivity, as compared with results for similar assays in batch reactors without recirculation. The observed bed expansion was due to the biogas production and the application of effluent recirculation led to a sludge bed contraction after all the substrates were degraded. The settleability of the anaerobic sludge improved by the introduction of effluent recirculation this effect being more pronounced for the higher loads.

In many animal species it is essential to recognize approach predators from complex, dynamic visual scenes and timely initiate escape behavior. Such sophisticated behaviours are often achieved with low neuronal complexity, such as in locusts, suggesting that emulating these biological models in artificial systems would enable the generation of similar complex behaviours with low computational overhead. On the other hand, artificial collision detection is a complex task that requires both real time data acquisition and important features extraction from a captured image. In order to accomplish this task, the algorithms used need to be fast to process the captured data and then perform real time decisions.Taking into account the previous considerations, neurorobotic models may provide a foundation for the development of more effective and autonomous devices/robots, based on an improved understanding of the biological basis of adaptive behavior. In this paper, we make a comparative analysis between the new computational model of a locust looming-detecting pathway and the model previously proposed by us. The obtained results proved the improvement provided by the pixel remapping in the model performance.