Analysis and evaluation of techniques for the extraction of classes in the ontology learning process

This paper analyzes and evaluates, in the context of Ontology learning, some techniques to identify and extract candidate terms to classes of a taxonomy. Besides, this work points out some inconsistencies that may be occurring in the preprocessing of text corpus, and proposes techniques to obtain good terms candidate to classes of a taxonomy.

A short motif in Drosophila SECIS Binding Protein 2 provides differential binding affinity to SECIS RNA hairpins

Selenoproteins contain the amino acid selenocysteine which is encoded by a UGA Sec codon. Recoding UGA Sec requires a complex mechanism, comprising the cis-acting SECIS RNA hairpin in the 3′UTR of selenoprotein mRNAs, and trans-acting factors. Among these, the SECIS Binding Protein 2 (SBP2) is central to the mechanism. SBP2 has been so far functionally characterized only in rats and humans. In this work, we report the characterization of the Drosophila melanogaster SBP2 (dSBP2). Despite its shorter length, it retained the same selenoprotein synthesis-promoting capabilities as the mammalian counterpart. However, a major difference resides in the SECIS recognition pattern: while human SBP2 (hSBP2) binds the distinct form 1 and 2 SECIS RNAs with similar affinities, dSBP2 exhibits high affinity toward form 2 only. In addition, we report the identification of a K (lysine)-rich domain in all SBP2s, essential for SECIS and 60S ribosomal subunit binding, differing from the well-characterized L7Ae RNA-binding domain. Swapping only five amino acids between dSBP2 and hSBP2 in the K-rich domain conferred reversed SECIS-binding properties to the proteins, thus unveiling an important sequence for form 1 binding.

Identifying protein-coding genes in genomic sequences

The vast majority of the biology of a newly sequenced genome is inferred from the set of encoded proteins. Predicting this set is therefore invariably the first step after the completion of the genome DNA sequence. Here we review the main computational pipelines used to generate the human reference protein-coding gene sets.

The Employment Precariousness Scale (EPRES): psychometric properties of a new tool for epidemiological studies among waged and salaried workers

Background: Despite the fact that labour market flexibility has resulted in an expansion of precarious employment in industrialized countries, to date there is limited empirical evidence about its health consequences. The Employment Precariousness Scale (EPRES) is a newly developed, theory-based, multidimensional questionnaire specifically devised for epidemiological studies among waged and salaried workers. Objective: To assess acceptability, reliability and construct validity of EPRES in a sample of waged and salaried workers in Spain. Methods: Cross-sectional study, using a sub-sample of 6.968 temporary and permanent workers from a population-based survey carried out in 2004-2005. The survey questionnaire was interviewer administered and included the six EPRES subscales, measures of the psychosocial work environment (COPSOQ ISTAS21), and perceived general and mental health (SF-36). Results: A high response rate to all EPRES items indicated good acceptability; Cronbach’s alpha coefficients, over 0.70 for all subscales and the global score, demonstrated good internal consistency reliability; exploratory factor analysis using principal axis analysis and varimax rotation confirmed the six-subscale structure and the theoretical allocation of all items. Patterns across known groups and correlation coefficients with psychosocial work environment measures and perceived health demonstrated the expected relations, providing evidence of construct validity. Conclusions: Our results provide evidence in support of the psychometric properties of EPRES, which appears to be a promising tool for the measurement of employment precariousness in public health research.

A variant in the gene FUT9 is associated with susceptibility to placental malaria infection

Malaria in pregnancy forms a substantial part of the worldwide burden of malaria, with an estimated annual death toll of up to 200,000 infants, as well as increased maternal morbidity and mortality. Studies of genetic susceptibility to malaria have so far focused on infant malaria, with only a few studies investigating the genetic basis of placental malaria, focusing only on a limited number of candidate genes. The aim of this study therefore was to identify novel host genetic factors involved in placental malaria infection. To this end we carried out a nested case-control study on 180 Mozambican pregnant women with placental malaria infection, and 180 controls within an intervention trial of malaria prevention. We genotyped 880 SNPs in a set of 64 functionally related genes involved in glycosylation and innate immunity. A SNP located in the gene FUT9, rs3811070, was significantly associated with placental malaria infection (OR = 2.31, permutation p-value = 0.028). Haplotypic analysis revealed a similarly strong association of a common haplotype of four SNPs including rs3811070. FUT9 codes for a fucosyl-transferase that is catalyzing the last step in the biosynthesis of the Lewis-x antigen, which forms part of the Lewis blood group-related antigens. These results therefore suggest an involvement of this antigen in the pathogenesis of placental malaria infection.

Allele variants in functional MicroRNA target sites of the neurotrophin-3 receptor gene (NTRK3) as susceptibility factors for anxiety disorders

Genetic and functional data indicate that variation in the expression of the neurotrophin-3 receptor gene (NTRK3) may have an impact on neuronal plasticity, suggesting a role for NTRK3 in the pathophysiology of anxiety disorders. MicroRNA (miRNA) posttranscriptional gene regulators act by base-pairing to specific sequence sites, usually at the 3'UTR of the target mRNA. Variants at these sites might result in gene expression changes contributing to disease susceptibility. We investigated genetic variation in two different isoforms of NTRK3 as candidate susceptibility factors for anxiety by resequencing their 3'UTRs in patients with panic disorder (PD), obsessive-compulsive disorder (OCD), and in controls. We have found the C allele of rs28521337, located in a functional target site for miR-485-3p in the truncated isoform of NTRK3, to be significantly associated with the hoarding phenotype of OCD. We have also identified two new rare variants in the 3'UTR of NTRK3, ss102661458 and ss102661460, each present only in one chromosome of a patient with PD. The ss102661458 variant is located in a functional target site for miR-765, and the ss102661460 in functional target sites for two miRNAs, miR-509 and miR-128, the latter being a brain-enriched miRNA involved in neuronal differentiation and synaptic processing. Interestingly, these two variants significantly alter the miRNA-mediated regulation of NTRK3, resulting in recovery of gene expression. These data implicate miRNAs as key posttranscriptional regulators of NTRK3 and provide a framework for allele-specific miRNA regulation of NTRK3 in anxiety disorders.

A targeted association study of immunity genes and networks suggests novel associations with placental malaria infection

A large proportion of the death toll associated with malaria is a consequence of malaria infection during pregnancy, causing up to 200,000 infant deaths annually. We previously published the first extensive genetic association study of placental malaria infection, and here we extend this analysis considerably, investigating genetic variation in over 9,000 SNPs in more than 1,000 genes involved in immunity and inflammation for their involvement in susceptibility to placental malaria infection. We applied a new approach incorporating results from both single gene analysis as well as gene-gene interactionson a protein-protein interaction network. We found suggestive associations of variants in the gene KLRK1 in the single geneanalysis, as well as evidence for associations of multiple members of the IL-7/IL-7R signalling cascade in the combined analysis. To our knowledge, this is the first large-scale genetic study on placental malaria infection to date, opening the door for follow-up studies trying to elucidate the genetic basis of this neglected form of malaria.

A unified treatment of optimum pilot overhead in multipath fading channels

The optimization of the pilot overhead in single-user wireless fading channels is investigated, and the dependence of this overhead on various system parameters of interest (e.g., fading rate, signal-to-noise ratio) is quantified. The achievable pilot-based spectral efficiency is expanded with respect to the fading rate about the no-fading point, which leads to an accurate order expansion for the pilot overhead. This expansion identifies that the pilot overhead, as well as the spectral efficiency penalty with respect to a reference system with genie-aided CSI (channel state information) at the receiver, depend on the square root of the normalized Doppler frequency. It is also shown that the widely-used block fading model is a special case of more accurate continuous fading models in terms of the achievable pilot-based spectral efficiency. Furthermore, it is established that the overhead optimization for multiantenna systems is effectively the same as for single-antenna systems with the normalized Doppler frequency multiplied by the number of transmit antennas.

Non-peaky signals in wideband fading channels: achievable bit rates and optimal bandwidth

In the context of fading channels it is well established that, with a constrained transmit power, the bit rates achievable by signals that are not peaky vanish as the bandwidth grows without bound. Stepping back from the limit, we characterize the highest bit rate achievable by such non-peaky signals and the approximate bandwidth where that apex occurs. As it turns out, the gap between the highest rate achievable without peakedness and the infinite-bandwidth capacity (with unconstrained peakedness) is small for virtually all settings of interest to wireless communications. Thus, although strictly achieving capacity in wideband fading channels does require signal peakedness, bit rates not far from capacity can be achieved with conventional signaling formats that do not exhibit the serious practical drawbacks associated with peakedness. In addition, we show that the asymptotic decay of bit rate in the absence of peakedness usually takes hold at bandwidths so large that wideband fading models are called into question. Rather, ultrawideband models ought to be used.

Discovering the campus together: a mobile and computer-based learning experience

One of the most relevant difficulties faced by first-year undergraduate students is to settle into the educational environment of universities. This paper presents a case study that proposes a computer-assisted collaborative experience designed to help students in their transition from high school to university. This is done by facilitating their first contact with the campus and its services, the university community, methodologies and activities. The experience combines individual and collaborative activities, conducted in and out of the classroom, structured following the Jigsaw Collaborative Learning Flow Pattern. A specific environment including portable technologies with network and computer applications has been developed to support and facilitate the orchestration of a flow of learning activities into a single integrated learning setting. The result is a Computer-Supported Collaborative Blended Learning scenario, which has been evaluated with first-year university students of the degrees of Software and Audiovisual Engineering within the subject Introduction to Information and Communications Technologies. The findings reveal that the scenario improves significantly students’ interest in their studies and their understanding about the campus and services provided. The environment is also an innovative approach to successfully support the heterogeneous activities conducted by both teachers and students during the scenario. This paper introduces the goals and context of the case study, describes how the technology was employed to conduct the learning scenario, the evaluation methods and the main results of the experience.

Reliable detection of directional couplings using rank statistics

To detect directional couplings from time series various measures based on distances in reconstructed state spaces were introduced. These measures can, however, be biased by asymmetries in the dynamics' structure, noise color, or noise level, which are ubiquitous in experimental signals. Using theoretical reasoning and results from model systems we identify the various sources of bias and show that most of them can be eliminated by an appropriate normalization. We furthermore diminish the remaining biases by introducing a measure based on ranks of distances. This rank-based measure outperforms existing distance-based measures concerning both sensitivity and specificity for directional couplings. Therefore, our findings are relevant for a reliable detection of directional couplings from experimental signals.

What can spike train distances tell us about the neural code?

Time scale parametric spike train distances like the Victor and the van Rossum distancesare often applied to study the neural code based on neural stimuli discrimination.Different neural coding hypotheses, such as rate or coincidence coding,can be assessed by combining a time scale parametric spike train distance with aclassifier in order to obtain the optimal discrimination performance. The time scalefor which the responses to different stimuli are distinguished best is assumed to bethe discriminative precision of the neural code. The relevance of temporal codingis evaluated by comparing the optimal discrimination performance with the oneachieved when assuming a rate code.We here characterize the measures quantifying the discrimination performance,the discriminative precision, and the relevance of temporal coding. Furthermore,we evaluate the information these quantities provide about the neural code. Weshow that the discriminative precision is too unspecific to be interpreted in termsof the time scales relevant for encoding. Accordingly, the time scale parametricnature of the distances is mainly an advantage because it allows maximizing thediscrimination performance across a whole set of measures with different sensitivitiesdetermined by the time scale parameter, but not due to the possibility toexamine the temporal properties of the neural code.

Cross recurrence quantification for cover song identification

There is growing evidence that nonlinear time series analysis techniques can be used to successfully characterize, classify, or process signals derived from realworld dynamics even though these are not necessarily deterministic and stationary. In the present study we proceed in this direction by addressing an important problem our modern society is facing, the automatic classification of digital information. In particular, we address the automatic identification of cover songs, i.e. alternative renditions of a previously recorded musical piece. For this purpose we here propose a recurrence quantification analysis measure that allows tracking potentially curved and disrupted traces in cross recurrence plots. We apply this measure to cross recurrence plots constructed from the state space representation of musical descriptor time series extracted from the raw audio signal. We show that our method identifies cover songs with a higher accuracy as compared to previously published techniques. Beyond the particular application proposed here, we discuss how our approach can be useful for the characterization of a variety of signals from different scientific disciplines. We study coupled Rössler dynamics with stochastically modulated mean frequencies as one concrete example to illustrate this point.

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.

Patient-specific computational hemodynamics of intracranial aneurysms from 3D rotational angiography and CT angiography: an in vivo reproducibility study

Patient-specific simulations of the hemodynamics in intracranial aneurysms can be constructed by using image-based vascular models and CFD techniques. This work evaluates the impact of the choice of imaging technique on these simulations