Rhamnolipids production from sucrose by engineered Saccharomyces cerevisiae

Biosurfactants are biological tensioactive agents that can be used in the cosmetic and food industries. Rhamnolipids are glycolipid biosurfactants naturally produced by Pseudomonas aeruginosa and are composed of one or two rhamnose molecules linked to beta-hydroxy fatty acid chains. These compounds are green alternatives to petrochemical surfactants, but their large-scale production is still in its infancy, hindered due to pathogenicity of natural producer, high substrate and purification costs and low yields and productivities. This study, for the first time, aimed at producing mono-rhamnolipids from sucrose by recombinant GRAS Saccharomyces cerevisiae strains. Six enzymes from P. aeruginosa involved in mono-rhamnolipid biosynthesis were functionally expressed in the yeast. Furthermore, its SUC2 invertase gene was disrupted and a sucrose phosphorylase gene from Pelomonas saccharophila was also expressed to reduce the pathway\u27s overall energy requirement. Two strains were constructed aiming to produce mono-rhamnolipids and the pathway\u27s intermediate dTDP-L-rhamnose. Production of both molecules was analyzed by confocal microscopy and mass spectrometry, respectively. These strains displayed, for the first time as a proof of concept, the potential of production of these molecules by a GRAS eukaryotic microorganism from an inexpensive substrate. These constructs show the potential to further improve rhamnolipids production in a yeast-based industrial bioprocess

Trigger and Aperture of the Surface Detector Array of the Pierre Auger Observatory

The surface detector array of the Pierre Auger Observatory consists of 1600 water-Cherenkov detectors, for the study of extensive air showers (EAS) generated by ultra-high-energy cosmic rays. We describe the trigger hierarchy, from the identification of candidate showers at the level of a single detector, amongst a large background (mainly random single cosmic ray muons), up to the selection of real events and the rejection of random coincidences. Such trigger makes the surface detector array fully efficient for the detection of EAS with energy above $3\times 10^{18}$ eV, for all zenith angles between 0$^\circ$ and 60$^\circ$, independently of the position of the impact point and of the mass of the primary particle. In these range of energies and angles, the exposure of the surface array can be determined purely on the basis of the geometrical acceptance.Comment: 29 pages, 12 figure

Ultrahigh energy neutrinos at the Pierre Auger observatory

The observation of ultrahigh energy neutrinos (UHEνs) has become a priority in experimental astroparticle physics. UHEνs can be detected with a variety of techniques. In particular, neutrinos can interact in the atmosphere (downward-going ν) or in the Earth crust (Earth-skimming ν), producing air showers that can be observed with arrays of detectors at the ground. With the surface detector array of the Pierre Auger Observatory we can detect these types of cascades. The distinguishing signature for neutrino events is the presence of very inclined showers produced close to the ground (i.e., after having traversed a large amount of atmosphere). In this work we review the procedure and criteria established to search for UHEνs in the data collected with the ground array of the Pierre Auger Observatory. This includes Earth-skimming as well as downward-going neutrinos. No neutrino candidates have been found, which allows us to place competitive limits to the diffuse flux of UHEνs in the EeV range and above.P. Abreu ... K. B. Barber ... J. A. Bellido ... R. W. Clay ... M. J. Cooper ... B. R. Dawson ... T. A. Harrison ... A. E. Herve ... V. C. Holmes ... J. Sorokin ... P. Wahrlich ... B. J. Whelan ... et al

Characterization of cast iron microstructure through the statistical fluctuation and fractal analyses of ultrasonic backscattered signals

This work aims to identify the different microstructures presented by cast iron, namely, lamellar, vermicular and spheroidal microstructures, through the statistical fluctuation and fractal analyses of backscattered ultrasonic signals. The signals were obtained with a broadband direct contact ultrasonic probe with a central frequency of 5 MHz. The statistical fluctuations of the ultrasonic signals were analyzed using Hurst and detrended-fluctuation analyses (DFA), and the fractal analyses were carried out by applying the minimal cover and box-counting techniques to the signals. The curves obtained for the statistical fluctuations and fractal analyses, as function of time window, were processed by using two pattern classification techniques, namely, principal-component analysis (PCA) and Karhunen-Lo\ue8ve expansion. For the Karhunen-Lo\ue8ve expansion, an approximately 100% success rate has been reached for the classification of the different microstructures, for the training and the testing sets of events. The results presented correspond to an average taken over a 100 randomly chosen sets of events. It is concluded that the statistical fluctuation and fractal analyses are effective additional tools for recognition of the different cast iron microstructures. \ua9 2011 American Institute of Physics.Peer reviewed: YesNRC publication: Ye