Supercritical fluid extraction of fish oil from fish by-products: A comparison with other extraction methods

Fish and fish by-products are the main natural source of omega-3 polyunsaturated fatty acids, especially EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid), both of them with a great importance in the food and pharmaceutical industries. Comparing to conventional fish oil extraction processes such as cold extraction, wet reduction or enzymatic extraction, supercritical fluid extraction with carbon dioxide under moderate conditions (25 MPa and 313 K) may be useful for reducing fish oil oxidation, especially when fish oil is rich in omega-3 such as salmon oil, and the amount of certain impurities, such as some species of arsenic. Furthermore, taking profit of the advantages of supercritical carbon dioxide as extractive solvent, a coupled extraction-fractionation process is proposed as a way to remove free fatty acids and improve fish oil quality, alternatively to physical and chemical refining procedures.     

Coordination Environment of Cu(II) Ions Bound to N-Terminal Peptide Fragments of Angiogenin Protein

Angiogenin (Ang) is a potent angiogenic factor, strongly overexpressed in patients affected by different types of cancers. The specific Ang cellular receptors have not been identified, but it is known that Ang–actin interaction induces changes both in the cell cytoskeleton and in the extracellular matrix. Most in vitro studies use the recombinant form (r-Ang) instead of the form that is normally present in vivo (“wild-type”, wt-Ang). The first residue of r-Ang is a methionine, with a free amino group, whereas wt-Ang has a glutamic acid, whose amino group spontaneously cyclizes in the pyro-glutamate form. The Ang biological activity is influenced by copper ions. To elucidate the role of such a free amino group on the protein–copper binding, we scrutinized the copper(II) complexes with the peptide fragments Ang(1–17) and AcAng(1–17), which encompass the sequence 1–17 of angiogenin (QDNSRYTHFLTQHYDAK-NH₂), with free amino and acetylated N-terminus, respectively. Potentiometric, ultraviolet-visible (UV-vis), nuclear magnetic resonance (NMR) and circular dichroism (CD) studies demonstrate that the two peptides show a different metal coordination environment. Confocal microscopy imaging of neuroblastoma cells with the actin staining supports the spectroscopic results, with the finding of different responses in the cytoskeleton organization upon the interaction, in the presence or not of copper ions, with the free amino and the acetylated N-terminus peptides.     

Optimal design of water pipeline networks for the development of shale gas resources

One of the major concerns in shale gas production is water management. Millions of gallons of water are injected to fracture each well and a significant amount returns to the surface as flowback. Operators are increasingly reusing flowback to reduce freshwater consumption and impaired water disposal. Because of this, networks of water pipelines in U.S. shales are growing fast. This work is aimed at addressing the optimal planning of shale gas operations in multiple wellpads together with the design of water distribution networks (WDN). We propose a multiperiod mixed-integer linear programming model to solve the challenging stay-or-mobilize trade-off. The proposed model permits to schedule operations at a detailed level, accounting for the WDN required to maximize the reuse of impaired water. We present illustrative examples involving up to 20 pads, 4 frac-crews, and 100 wells developed over 1 year, showing that the design of the WDN can be effectively optimized.     

Interfacial area and mass transfer in carbon dioxide absorption in TEA aqueous solutions in a bubble column reactor

The hydrodynamic behaviour and mass transfer of carbon dioxide removal process by aqueous solutions of triethanolamine (TEA) are analysed. The experiments were made in a bubble column reactor (BCR) as gas–liquid contactor. The interfacial area and mass transfer coefficient were calculated by using a photographic method based on the bubble diameter determination. The influence of operation conditions, liquid phase nature and chemical reaction on the mass transfer coefficient and gas–liquid interfacial area has been also analysed.     

Age and productivity as determinants of firm survival over the industry life cycle

This paper contributes to fill the gap between the literature on the determinants of firm survival and the empirical works on the industry life cycle (ILC). Using a representative sample of Spanish firms with 10 or more employees over the period 1993–2009, the role played by firm age and productivity in firm survival is empirically analysed across three stages of the life cycle of forty-seven 3-digit manufacturing sectors. In the ‘early’ stage of the ILC, firm age is negatively correlated with hazard rates while firm productivity is not. Firm productivity is associated with lower hazard in the ‘mature’ stage of the ILC, when competition is primarily efficiency-driven, while firm age does not play a significant role for firm survival. In the ‘intermediate’ stage, both age and productivity play a role in reducing firms’ hazard rates.     

An optimization method for learning statistical classifiers in structural reliability

Monte Carlo simulation is a general and robust method for structural reliability analysis, affected by the serious efficiency problem consisting in the need of computing the limit state function a very large number of times. In order to reduce this computational effort the use of several kinds of solver surrogates has been proposed in the recent past. Proposals include the Response Surface Method (RSM), Neural Networks (NN), Support Vector Machines (SVM) and several other methods developed in the burgeoning field of Statistical Learning (SL). Many of these techniques can be employed either for function approximation (regression approach) or for pattern recognition (classification approach). This paper concerns the use of these devices for discriminating samples into safe and failure classes using the classification approach, because it constitutes the core of Monte Carlo simulation as applied to reliability analysis as such. Due to the flexibility of most SL methods, a critical step in their use is the generation of the learning population, as it affects the generalization capacity of the surrogate. To this end it is first demonstrated that the optimal population from the information viewpoint lies around in the vicinity of the limit state function. Next, an optimization method assuring a small as well as highly informative learning population is proposed on this basis. It consists in generating a small initial quasi-random population using Sobol sequence for triggering a Particle Swarm Optimization (PSO) performed over an iteration-dependent cost function defined in terms of the limit state function. The method is evaluated using SVM classifiers, but it can be readily applied also to other statistical classification techniques because the distinctive feature of the SVM, i.e. the margin band, is not actively used in the algorithm. The results show that the method yields results for the probability of failure that are in very close agreement with Monte Carlo simulation performed on the original limit state function and requiring a small number of learning samples.