Group Study of Simulated Driving fMRI Data by Multiset Canonical Correlation Analysis

In this work, we apply a novel statistical method, multiset canonical correlation analysis (M-CCA), to study a group of functional magnetic resonance imaging (fMRI) datasets acquired during simulated driving task. The M-CCA method jointly decomposes fMRI datasets from different subjects/sessions into brain activation maps and their associated time courses, such that the correlation in each group of estimated activation maps across datasets is maximized. Therefore, the functional activations across all datasets are extracted in the order of consistency across different dataset. On the other hand, M-CCA preserves the uniqueness of the functional maps estimated from each dataset by avoiding concatenation of different datasets in the analysis. Hence, the cross-dataset variation of the functional activations can be used to test the hypothesis of functional-behavioral association. In this work, we study 120 simulated driving fMRI datasets and identify parietal-occipital regions and frontal lobe as the most consistently engaged areas across all the subjects and sessions during simulated driving. The functional-behavioral association study indicates that all the estimated brain activations are significantly correlated with the steering operation during the driving task. M-CCA thus provides a new approach to investigate the complex relationship between the brain functions and multiple behavioral variables, especially in naturalistic tasks as demonstrated by the simulated driving study.     

Use of Repetitive Sequences for Molecular and Cytogenetic Characterization of Avena Species from Portugal

Genomic diversity of Portuguese accessions of Avena species—diploid A. strigosa and hexaploids A. sativa and A. sterilis—was evaluated through molecular and cytological analysis of 45S rDNA, and other repetitive sequences previously studied in cereal species—rye subtelomeric sequence (pSc200) and cereal centromeric sequence (CCS1). Additionally, retrotransposons and microsatellites targeting methodologies—IRAP (inter-retrotransposon amplified polymorphism) and REMAP (retrotransposon-microsatellite amplified polymorphism)—were performed. A very high homology was detected for ribosomal internal transcribed sequences (ITS1 and ITS2) between the species analyzed, although nucleolar organizing regions (NOR) fluorescent in situ hybridization (FISH) analysis revealed distinct number of Nor loci between diploid and hexaploid species. Moreover, morphological diversity, evidenced by FISH signals with different sizes, was observed between distinct accessions within each species. pSc200 sequences were for the first time isolated from Avena species but proven to be highly similar in all genotypes analyzed. The use of primers designed for CCS1 unraveled a sequence homologous to the Ty3/gypsy retrotransposon Cereba, that was mapped to centromeric regions of diploid and hexaploid species, being however restricted to the more related A and D haplomes. Retrotransposon-based methodologies disclosed species- and accessions-specific bands essential for the accurate discrimination of all genotypes studied. Centromeric, IRAP and REMAP profiles therefore allowed accurate assessment of inter and intraspecific variability, demonstrating the potential of these molecular markers on future oat breeding programs.     

Speech and noise power estimation using Gamma modeling

In speech enhancement, having an accurate estimation of the power of the speech and noise signals forming the noisy observation is critical, as it can highly affect the performance of the enhancement algorithm. A method is introduced in which the distributions of the power of the speech and noise periodograms are modeled using the Gamma distribution to extract their shape parameters. These shape parameters are later used in the observed noisy speech to estimate the power when forming speech and noise periodograms. This method results in more accurate and faster power estimation with respect to the well‐known minimum statistics power estimation algorithm and together with the maximum a posteriori speech enhancement algorithm exhibits good speech enhancement performance.     

Gram-Negative Flagella Glycosylation

Protein glycosylation had been considered as an eccentricity of a few bacteria. However, through advances in analytical methods and genome sequencing, it is now established that bacteria possess both N-linked and O-linked glycosylation pathways. Both glycosylation pathways can modify multiple proteins, flagellins from Archaea and Eubacteria being one of these. Flagella O-glycosylation has been demonstrated in many polar flagellins from Gram-negative bacteria and in only the Gram-positive genera Clostridium and Listeria. Furthermore, O-glycosylation has also been demonstrated in a limited number of lateral flagellins. In this work, we revised the current advances in flagellar glycosylation from Gram-negative bacteria, focusing on the structural diversity of glycans, the O-linked pathway and the biological function of flagella glycosylation.     

Analyzing probabilistic pushdown automata

The paper gives a summary of the existing results about algorithmic analysis of probabilistic pushdown automata and their subclasses.     

Automated resolution of connector architectures using constraint solving (ARCAS method)

In current software systems, connectors play an important role by encapsulating the communication and coordination logic. Since they share common patterns (elements) depending on characteristics of the connections, the elements can be predefined and reused. A method of connector implementation based on a composition of predefined elements naturally comprises two steps: resolution of the connector architecture, and creation of the actual connector code based on the architecture. However, manual resolution of a connector architecture is very difficult due to the number of factors to be considered. Thus, the challenge is to come up with an automated method, able to address all the important factors. In this paper, we present a method for automated resolution of connector architectures based on constraint solving techniques. We exploit a propositional logic with relational calculus for defining a connector theory, a constraint specification reflecting both the predefined parts and the important resolution factors, and employ a constraint solver to find a suitable connector architecture as a model of the theory. As a proof of the concept, we show how the theory can be captured in the Alloy language and resolved via the Alloy Analyzer.     

A framework for computing asset-sighting distance

Safety critical railway assets need to be visible from prescribed distances, ensuring safety. Traditional methods for measuring these sighting distances involve manual labour and disruption of rail services. Drawing parallels with other areas, efficiency and cost of such a task can be improved with automation. Hence, in this work, we describe a framework providing such automation, using image frames captured from video equipment as input. Any such framework needs to meet a set of associated challenges, including: determining the image position of decreasing sized assets as they appear farther from the observer, extracting three-dimensional positioning information (relative to motion) from two-dimensional video information and determining when an asset can no longer be seen (i.e. the sighting distance). Results show that the methods included in this framework perform better than a traditional method and information regarding asset-sighting distance is accurately computed.     

Investigation on PEM water electrolysis cell design and components for a HyCon solar hydrogen generator

Hydrogen as a secondary energy carrier promises a large potential as a long term storage for fluctuating renewable energies. In this sense a highly efficient solar hydrogen generation is of great interest especially in southern countries having high solar irradiation. The patented Hydrogen Concentrator (HyCon) concept yields high efficiencies combining multi-junction solar cells with proton exchange (PEM) membrane water electrolysis. In this work, a special PEM electrolysis cell for the HyCon concept was developed and investigated. It is shown that the purpose-made PEM cell shows a high performance using a titanium hybrid fiber sinter function both as a porous transport layer and flow field. The electrolysis cell shows a high performance with 1.83 V at 1 A/cm² and 24 °C working under natural convection with a commercially available catalyst coated membrane. A theoretical examination predicts a total efficiency for the HyCon module from sunlight to hydrogen of approximately 19.5% according to the higher heating value.