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.     

Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy

We present a method for autonomous learning of dextrous manipulation skills with multifingered robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few basic manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy, which is robust to the noise normally present in real-world complex robotic tasks. Given the difficulty of modeling and simulating accurately the interactions of multiple fingers and an object, and to ensure that the learned skills are applicable in the real world, our system does not rely on simulation; all the experimentation is performed by a physical robot, in this case the 16-degree-of-freedom Utah/MIT hand. Experimental results show that accurate dextrous manipulation skills can be learned by the robot in a short period of time. We also show the application of the learned primitives to perform an assembly task and how the primitives generalize to objects that are different from those used during the learning phase.     

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.     

Characterization of commercial double-walled carbon nanotube material: composition, structure, and heat capacity

A purified commercial double-walled carbon nanotube (DWCNT) sample was investigated by transmission electron microscopy (TEM), thermogravimetry (TG), and Raman spectroscopy. Moreover, the heat capacity of the DWCNT sample was determined by temperature-modulated differential scanning calorimetry in the range of temperature between −50 and 290 °C. The main thermo-oxidation characterized by TG occurred at 474 °C with the loss of 90 wt% of the sample. Thermo-oxidation of the sample was also investigated by high-resolution TG, which indicated that a fraction rich in carbon nanotube represents more than 80 wt% of the material. Other carbonaceous fractions rich in amorphous coating and graphitic particles were identified by the deconvolution procedure applied to the derivative of TG curve. Complementary structural data were provided by TEM and Raman studies. The information obtained allows the optimization of composites based on this nanomaterial with reliable characteristics.     

New approach to immobilization and specific-sequence detection of nucleic acids based on poly(4-hydroxyphenylacetic acid)

Immobilization and hybridization of oligonucleotides or specific-gene PCR product (DENV-1), a conserved genomic sequence of the dengue virus, onto graphite electrode modified with poly(4-hydroxyphenylacetic acid), were carried out with success using both direct electrochemical oxidation of guanine or redox electroactive indicator ethidium bromide.Studies of oligonucleotides hybridization with the complementary target showed a decrease of both guanosine and adenosine current peaks, when compared with the peak previously obtained before the hybridization. Immobilized ssDNA, DENV-1, was hybridized with various concentrations of target DNA. The interaction between DENV-1 hybridized onto the modified graphite electrodes surface and the intercalator, ethidium bromide, was observed by differential pulse voltammetry, monitoring the current change generated to the DNA intercalator accumulated onto the modified electrode after DNA hybridization. For the determination of complementary target, the proposed method exhibited a good dynamic range (12–42 nmol L^? 1) and a low detection limit (7.12 nmol L^? 1).AFM images showed that the oligonucleotides or single-stranded DNA, DENV-1, before hybridization, had roughness values lower than the double stranded obtained after hybridization.The new surface obtained in these work, as well as the possibility of utilization of the same to monitor hybridization events is a promising strategy for the development of DNA electrochemical biosensors.     

Improving scientific application execution on android mobile devices via code refactorings

The increasing number of mobile devices with ever‐growing capabilities makes them useful for running scientific applications. However, these applications have high computational demands, whereas mobile devices have limited capabilities when compared with non‐mobile devices. More importantly, mobile devices rely on batteries for their power supply. We initially measure the battery consumption of different versions of known micro‐benchmarks representing common programming primitives found in scientific applications. Then, we analyze the performance of such micro‐benchmarks in CPU‐intensive mobile applications. We apply good programming practices and code refactorings to reduce battery consumption of scientific mobile applications. Our results show the reduction in energy usage from applying these refactorings to three scientific applications, and we consequently propose guidelines for high‐performance computing applications. Our focus is on Android, the dominant mobile operating system. As a long‐term contribution, our results represent one more step in the progress towards hybrid distributed infrastructures comprising fixed and mobile nodes, that is, the so‐called mobile grids. Copyright © 2016 John Wiley & Sons, Ltd.     

Genetic LGALS1 Variants Are Associated with Heterogeneity in Galectin-1 Serum Levels in Patients with Early Arthritis

Galectin 1 (Gal1) exerts immunomodulatory effects leading to therapeutic effects in autoimmune animal models. Patients with rheumatoid arthritis have been reported to show higher Gal1 serum levels than the healthy population. Our study aimed to find genetic variants on the Gal1 gene (LGALS1) modulating its expression and/or clinical features in patients with early arthritis (EA). LGALS1 was sequenced in 53 EA patients to characterize all genetic variants. Then, we genotyped rs9622682, rs929039, and rs4820293, which covered the main genetic variation in LGALS1, in 532 EA patients. Gal1 and IL-6 serum levels were measured by ELISA and Gal1 also by western blot (WB) in lymphocytes from patients with specific genotypes. Once disease activity improved with treatment, patients with at least one copy of the minor allele in rs9622682 and rs929039 or those with GG genotype in rs4820293 showed significantly higher Gal1 serum levels (p < 0.05). These genotypic combinations were also associated with higher Gal1 expression in lymphocytes by WB and lower IL-6 serum levels in EA patients. In summary, our study suggests that genetic variants studied in LGALS1 can explain heterogeneity in Gal1 serum levels showing that patients with higher Gal1 levels have lower serum IL-6 levels.