Stepwise dissection and visualization of the catalytic mechanism of haloalkane dehalogenase LinB using molecular dynamics simulations and computer graphics

The different steps of the dehalogenation reaction carried out by LinB on three different substrates have been characterized using a combination of quantum mechanical calculations and molecular dynamics simulations. This has allowed us to obtain information in atomic detail about each step of the reaction mechanism, that is, substrate entrance and achievement of the near-attack conformation, transition state stabilization within the active site, halide stabilization, water molecule activation and subsequent hydrolytic attack on the ester intermediate with formation of alcohol, and finally product release. Importantly, no bias or external forces were applied during the whole procedure so that both intermediates and products were completely free to sample configuration space in order to adapt to the plasticity of the active site and/or search for an exit. Differences in substrate reactivity were found to be correlated with the ease of adopting the near-attack conformation and two different exit pathways were found for product release that do not interfere with substrate entrance. Additional support for the different entry and exit pathways was independently obtained from an examination of the enzyme's normal modes.     

Guidelines for the incremental identification of aspects in requirements specifications

The desired principle of separation of concerns in software development can be jeopardized by the so-called crosscutting concerns, which tend to be scattered over (and tangled with) the functionality of the modular units of a system. The correct identification of such concerns (and their encapsulation into separate artifacts) is thereby considered a way to improve software understanding and evolution. Pursuing a proper management of concerns from the requirements engineering stage can greatly benefit the entire software life-cycle. In this paper, we propose conceptual guidelines on how to perform the identification of crosscutting concerns in the process of building requirements specifications. We argue that the identification must be carried out in an incremental way, to encapsulate apart the crosscutting concerns even if they have not emerged completely yet.

Computing the homology of groups: The geometric way

In this paper, we present several algorithms related with the computation of the homology of groups, from a geometric perspective (that is to say, carrying out the calculations by means of simplicial sets and using techniques of Algebraic Topology). More concretely, we have developed some algorithms which, making use of the effective homology   method, construct the homology groups of Eilenberg–MacLane spaces K(G,1)$K(G,1)$ for different groups G$G$, allowing one in particular to determine the homology groups of G$G$.     

Pollution Buildup on Road Surfaces

Samples of sediment found on an urban road in Aberdeen (Scotland) were collected by washing designated surfaces. This method, called the “wet” method, was capable of collecting sediment of the smallest particle size range that is normally left behind by traditional sampling techniques using dry vacuuming. Over 17 months (mainly on a weekly basis), 66 samples were collected and analyzed for sediment loading, particle size distribution, concentration of heavy metals (Zn, Cu, Pb, Cd) in four sediment size fractions, and several dissolved pollutants in the effluent collected from washing (NO3?,?SO42?,?PO43?,?Cl?,?F?,?NH4+, total organic carbon, total carbon). Standard statistical methods, including multiple regressions, were used to determine relationships amongst different sediment characteristics. It was found that sediment loading, as well as concentrations of Cl? and SO42?, were highest in the winter months, especially when snow was present on the road surface. It was observed that 66% of total road sediment loading was found within a 0.5 m strip next to the curb. The average sediment particle sizes found were smaller than those previously recorded in the literature. As expected, the concentrations of heavy metals were highest in the smallest particle size fraction analyzed (<63?μm), and this occurred during the summer months when less sediment was available on the street surface. The antecedent dry weather period had a very weak and negative influence on the loading rate of the smallest particle size fraction next to the curb.     

Influence diagnostics in regression with complex designs through conditional bias

One of the areas of Statistics in which the influence analysis has been widely studied is the multiple linear regression model. Nevertheless, the influence diagnostics proposed in this context cannot be applied to regression in complex survey, under randomized inference, since the i.i.d. case does not incorporate any probability weighting or population structure, such as clustering, stratification or measures of size into the analysis. In this paper we introduce some influence diagnostics in regression in complex survey. They are built on the conditional bias concept (Moreno-Rebollo et al., 1999). We emphasize the similarities and differences of the proposed measures with respect to the existing ones for the i.i.d. case. Research partly supported by the Spanish MCYT grant BFM2001-3844 and Spanish MEC grant MTM2004-0111433.     

ZRC – A Refinement Calculus for Z

The fact that Z is a specification language only, with no associated program development method, is a widely recognised problem. As an answer to that, we present ZRC, a refinement calculus based on Morgan's work that incorporates the Z notation and follows its style and conventions. This work builds upon existing refinement techniques for Z, but distinguishes itself mainly in that ZRC is completely formalised. In this paper, we explain how programs can be derived from Z specifications using ZRC. We present ZRC-L, the language of our calculus, and its conversion laws, which are concerned with the transformation of Z schemas into programs of this language. Moreover, we present the weakest precondition semantics of ZRC-L, which is the basis for the derivation of the laws of ZRC. More than a refinement calculus, ZRC is a theory of refinement for Z. Received July 1997 / Accepted in revised form October 1998     

Forecasting with unequally spaced data by a functional principal component approach

The Principal Component Regression model of multiple responses is extended to forccast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data.     

Roles of the Unsaturated Fatty Acid Docosahexaenoic Acid in the Central Nervous System: Molecular and Cellular Insights

Fatty acids (FAs) are essential components of the central nervous system (CNS), where they exert multiple roles in health and disease. Among the FAs, docosahexaenoic acid (DHA) has been widely recognized as a key molecule for neuronal function and cell signaling. Despite its relevance, the molecular pathways underlying the beneficial effects of DHA on the cells of the CNS are still unclear. Here, we summarize and discuss the molecular mechanisms underlying the actions of DHA in neural cells with a special focus on processes of survival, morphological development, and synaptic maturation. In addition, we examine the evidence supporting a potential therapeutic role of DHA against CNS tumor diseases and tumorigenesis. The current results suggest that DHA exerts its actions on neural cells mainly through the modulation of signaling cascades involving the activation of diverse types of receptors. In addition, we found evidence connecting brain DHA and ω-3 PUFA levels with CNS diseases, such as depression, autism spectrum disorders, obesity, and neurodegenerative diseases. In the context of cancer, the existing data have shown that DHA exerts positive actions as a coadjuvant in antitumoral therapy. Although many questions in the field remain only partially resolved, we hope that future research may soon define specific pathways and receptor systems involved in the beneficial effects of DHA in cells of the CNS, opening new avenues for innovative therapeutic strategies for CNS diseases.     

An approach to accidents modeling based on compounds road environments

The most common approach to study the influence of certain road features on accidents has been the consideration of uniform road segments characterized by a unique feature. However, when an accident is related to the road infrastructure, its cause is usually not a single characteristic but rather a complex combination of several characteristics. The main objective of this paper is to describe a methodology developed in order to consider the road as a complete environment by using compound road environments, overcoming the limitations inherented in considering only uniform road segments. The methodology consists of: dividing a sample of roads into segments; grouping them into quite homogeneous road environments using cluster analysis; and identifying the influence of skid resistance and texture depth on road accidents in each environment by using generalized linear models. The application of this methodology is demonstrated for eight roads. Based on real data from accidents and road characteristics, three compound road environments were established where the pavement surface properties significantly influence the occurrence of accidents. Results have showed clearly that road environments where braking maneuvers are more common or those with small radii of curvature and high speeds require higher skid resistance and texture depth as an important contribution to the accident prevention.