Cloning and sequence analysis of the Pichia pastoris TRP1, IPP1 and HIS3 genes

The Pichia pastoris TRP1 and HIS3 genes were cloned by complementation of the Saccharomyces cerevisiae trp1 and his3 mutants, respectively, and their nucleotide sequence was determined. The P. pastoris TRP1 gene includes an open reading frame (ORF) of 714 nucleotides corresponding to a polypeptide of 237 amino acids whose sequence shares about 40% identity with that of TRP1 encoding proteins in other yeast species. DNA sequencing showed that an ORF of 858 nucleotides, encoding a protein of 285 amino acids with high homology to inorganic pyrophosphatases (IPP1), is located downstream of the P. pastoris TRP1 gene. Both genes converge in this chromosomal region, showing a genetic organization analogous to that found in the Kluyveromyces lactis genome. The P. pastoris HIS3 gene possesses an ORF of 675 nucleotides, encoding a polypeptide of 224 amino acids which shows 74·1% identity to the homologous S. cerevisiae protein. The hexameric consensus GCN4 binding sequence (TGACTC), characteristic of many amino acid biosynthetic genes, is present in the promoter region. The TRP1 and IPP1 sequences were deposited in the EMBL databank under Accession Number AJ001000. The Accession Number of the HIS3 gene is U69170. © 1998 John Wiley & Sons, Ltd.     

Intracardiac thrombus trapped in a patent foramen ovale

We describe a case of impending paradoxical embolism due to a thrombus trapped in a patent foramen ovale in a 22-year-old woman. Transthoracic and transesophageal echocardiography detected the thrombus. She was operated on and discharged asymptomatic.     

A Large Strains Finite Element Multiscale Approach

A novel formulation for multiscale finite element analysis of multiphase solids undergoing large strains is proposed in this paper. Within the described homogenization technique no constitutive assumptions are made at the macrolevel. A crucial aspects of the approach is the modeling of antiperiodic traction on the boundary of the representative volume element, condensation technique and the formulation performed on a deformation-driven context whereby the macroscopic deformation gradient is prescribed. Numerical tests on solids with voids demonstrated the robustness of the technique.     

Carbon dioxide absorption by ammonia intensified with membrane contactors

Membrane gas absorption technology is a promising alternative for CO₂ removal from post-combustion coal-fired flue gases. This study examines an alternative which consists in absorbing carbon dioxide by ammonia aqueous solution in a membrane contactor to improve the capture processes and to intensify the gas–liquid transfer. Absorption measurements through a membrane contactor have been made. The influence of the material nature constituting the membrane and operating parameters on the capture efficiency has been studied. The potentialities of dense skin membrane contactors are discussed with regard to both increased CO₂ mass transfer performances and mitigation of ammonia volatilization. The results have shown that it is possible to capture CO₂ from ammonia through a membrane with capture efficiency greater than 90 %. The membrane limits ammonia losses but does not eliminate it. The experimental results are used to calculate an intensification factor of 5, which represents the comparison between the membrane overall absorption rate to that of the column.     

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.     

Multimodal Interface Technologies for UAV Ground Control Stations

This paper examines different technologies that can be applied in the design and development of a ground control station for Unmanned Aerial Vehicles (UAVs) equipped with multimodal interfaces. Multimodal technologies employ multiple sensory channels/modalities for information transmission as well as for system control. Examples of these technologies could be haptic feedback, head tracking, auditory information (3D audio), voice control, tactile displays, etc. The applicability and benefits of those technologies is analyzed for a task consisting in the acknowledgement of alerts in an UAV ground control station composed by three screens and managed by a single operator. For this purpose, several experiments were conducted with a group of individuals using different combinations of modal conditions (visual, aural and tactile).