Aplicación de las licencias de Creative Commons en el ámbito educativo

En este texto se presentan las licencias que ofrece la organización Creative Commons para gestionar los derechos de propiedad intelectual de una manera distinta al tradicional “todos los derechos reservados”. Estas licencias inspiradas en el movimientodel software libre se ofrecen gratuitamente para todos aquellos autores que quieran compartir sus obras permitiendo determinados usos con algunas condiciones sin tener que pedir permiso previo. Además se hace un repaso a la aplicación de estas licencias en el ámbito educativo mostrando ejemplos y analizando su uso con respecto al tipo de proyecto y el tipo de licencias.

Las licencias de Creative Commons en las revistas científicas

Comunicació presentada a la "Jornada informativa Grupo de investigación Acceso Abierto a la Ciencia". Barcelona, 3 de marzo de 2010

Differences in the evolutionary history of disease genes affected by dominant or recessive mutations

Background: Global analyses of human disease genes by computational methods have yielded important advances in the understanding of human diseases. Generally these studies have treated the group of disease genes uniformly, thus ignoring the type of disease-causing mutations (dominant or recessive). In this report we present a comprehensive study of the evolutionary history of autosomal disease genes separated by mode of inheritance.Results: We examine differences in protein and coding sequence conservation between dominant and recessive human disease genes. Our analysis shows that disease genes affected by dominant mutations are more conserved than those affected by recessive mutations. This could be a consequence of the fact that recessive mutations remain hidden from selection while heterozygous. Furthermore, we employ functional annotation analysis and investigations into disease severity to support this hypothesis. Conclusion: This study elucidates important differences between dominantly- and recessively-acting disease genes in terms of protein and DNA sequence conservation, paralogy and essentiality. We propose that the division of disease genes by mode of inheritance will enhance both understanding of the disease process and prediction of candidate disease genes in the future.

Comparative analysis of amino acid repeats in rodents and humans

Amino acid tandem repeats, also called homopolymeric tracts, are extremely abundant in eukaryotic proteins. To gain insight into the genome-wide evolution of these regions in mammals, we analyzed the repeat content in a large data set of rat-mouse-human orthologs. Our results show that human proteins contain more amino acid repeats than rodent proteins and that trinucleotide repeats are also more abundant in human coding sequences. Using the human species as an outgroup, we were able to address differences in repeat loss and repeat gain in the rat and mouse lineages. In this data set, mouse proteins contain substantially more repeats than rat proteins, which can be at least partly attributed to a higher repeat loss in the rat lineage. The data are consistent with a role for trinucleotide slippage in the generation of novel amino acid repeats. We confirm the previously observed functional bias of proteins with repeats, with overrepresentation of transcription factors and DNA-binding proteins. We show that genes encoding amino acid repeats tend to have an unusually high GC content, and that differences in coding GC content among orthologs are directly related to the presence/absence of repeats. We propose that the different GC content isochore structure in rodents and humans may result in an increased amino acid repeat prevalence in the human lineage.

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential—a phenomenon referred to as “phase-of-firing coding” (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions—only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (~10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

Creative and Multidisciplinary Etching in the Digital Age

In our capacity as creative artists, researchers and fine arts professors, most part of our activity focuses on the relationships between people and creativity, technology, and resources, and, most frequently, what we aim to offer are new enjoyable and subversive ways of interacting with these three fields. As art teachers, we ask 'why and how' to teach dynamic bearing in mind that digital technology will undoubtedly impact on contemporary art practice.

The genome and development-dependent transcriptomes of Pyronema confluens: a window into fungal evolution

Fungi are a large group of eukaryotes found in nearly all ecosystems. More than 250 fungal genomes have already been sequenced, greatly improving our understanding of fungal evolution, physiology, and development. However, for the Pezizomycetes, an early-diverging lineage of filamentous ascomycetes, there is so far only one genome available, namely that of the black truffle, Tuber melanosporum, a mycorrhizal species with unusual subterranean fruiting bodies. To help close the sequence gap among basal filamentous ascomycetes, and to allow conclusions about the evolution of fungal development, we sequenced the genome and assayed transcriptomes during development of Pyronema confluens, a saprobic Pezizomycete with a typical apothecium as fruiting body. With a size of 50 Mb and ~13,400 protein-coding genes, the genome is more characteristic of higher filamentous ascomycetes than the large, repeat-rich truffle genome; however, some typical features are different in the P. confluens lineage, e.g. the genomic environment of the mating type genes that is conserved in higher filamentous ascomycetes, but only partly conserved in P. confluens. On the other hand, P. confluens has a full complement of fungal photoreceptors, and expression studies indicate that light perception might be similar to distantly related ascomycetes and, thus, represent a basic feature of filamentous ascomycetes. Analysis of spliced RNA-seq sequence reads allowed the detection of natural antisense transcripts for 281 genes. The P. confluens genome contains an unusually high number of predicted orphan genes, many of which are upregulated during sexual development, consistent with the idea of rapid evolution of sex-associated genes. Comparative transcriptomics identified the transcription factor gene pro44 that is upregulated during development in P. confluens and the Sordariomycete Sordaria macrospora. The P. confluens pro44 gene (PCON_06721) was used to complement the S. macrospora pro44 deletion mutant, showing functional conservation of this developmental regulator.

Block structured dynamics and neuronal coding

When certain control parameters of nervous cell models are varied, complex bifurcation structures develop in which the dynamical behaviors available appear classified in blocks, according to criteria of dynamical likelihood. This block structured dynamics may be a clue to understand how activated neurons encode information by firing spike trains of their action potentials.

Hybrid turbo FEC/ARQ systems and distributed space-time coding for cooperative transmission

Cooperative transmission can be seen as a "virtual" MIMO system, where themultiple transmit antennas are in fact implemented distributed by the antennas both at the source and the relay terminal. Depending on the system design, diversity/multiplexing gainsare achievable. This design involves the definition of the type of retransmission (incrementalredundancy, repetition coding), the design of the distributed space-time codes, the errorcorrecting scheme, the operation of the relay (decode&forward or amplify&forward) and thenumber of antennas at each terminal. Proposed schemes are evaluated in different conditionsin combination with forward error correcting codes (FEC), both for linear and near-optimum(sphere decoder) receivers, for its possible implementation in downlink high speed packetservices of cellular networks. Results show the benefits of coded cooperation over directtransmission in terms of increased throughput. It is shown that multiplexing gains areobserved even if the mobile station features a single antenna, provided that cell wide reuse of the relay radio resource is possible.

Complete DNA sequence of Kuraishia capsulata illustrates novel genomic features among budding yeasts (Saccharomycotina)

The numerous yeast genome sequences presently available provide a rich source of information for functional as well as evolutionary genomics but unequally cover the large phylogenetic diversity of extant yeasts. We present here the complete sequence of the nuclear genome of the haploid-type strain of Kuraishia capsulata (CBS1993(T)), a nitrate-assimilating Saccharomycetales of uncertain taxonomy, isolated from tunnels of insect larvae underneath coniferous barks and characterized by its copious production of extracellular polysaccharides. The sequence is composed of seven scaffolds, one per chromosome, totaling 11.4 Mb and containing 6,029 protein-coding genes, ~13.5% of which being interrupted by introns. This GC-rich yeast genome (45.7%) appears phylogenetically related with the few other nitrate-assimilating yeasts sequenced so far, Ogataea polymorpha, O. parapolymorpha, and Dekkera bruxellensis, with which it shares a very reduced number of tRNA genes, a novel tRNA sparing strategy, and a common nitrate assimilation cluster, three specific features to this group of yeasts. Centromeres were recognized in GC-poor troughs of each scaffold. The strain bears MAT alpha genes at a single MAT locus and presents a significant degree of conservation with Saccharomyces cerevisiae genes, suggesting that it can perform sexual cycles in nature, although genes involved in meiosis were not all recognized. The complete absence of conservation of synteny between K. capsulata and any other yeast genome described so far, including the three other nitrate-assimilating species, validates the interest of this species for long-range evolutionary genomic studies among Saccharomycotina yeasts.

The genome of the recently domesticated crop plant sugar beet (Beta vulgaris)

Sugar beet (Beta vulgaris ssp. vulgaris) is an important crop of temperate climates which provides nearly 30% of the world's annual sugar production and is a source for bioethanol and animal feed. The species belongs to the order of Caryophylalles, is diploid with 2n = 18 chromosomes, has an estimated genome size of 714-758 megabases and shares an ancient genome triplication with other eudicot plants. Leafy beets have been cultivated since Roman times, but sugar beet is one of the most recently domesticated crops. It arose in the late eighteenth century when lines accumulating sugar in the storage root were selected from crosses made with chard and fodder beet. Here we present a reference genome sequence for sugar beet as the first non-rosid, non-asterid eudicot genome, advancing comparative genomics and phylogenetic reconstructions. The genome sequence comprises 567 megabases, of which 85% could be assigned to chromosomes. The assembly covers a large proportion of the repetitive sequence content that was estimated to be 63%. We predicted 27,421 protein-coding genes supported by transcript data and annotated them on the basis of sequence homology. Phylogenetic analyses provided evidence for the separation of Caryophyllales before the split of asterids and rosids, and revealed lineage-specific gene family expansions and losses. We sequenced spinach (Spinacia oleracea), another Caryophyllales species, and validated features that separate this clade from rosids and asterids. Intraspecific genomic variation was analysed based on the genome sequences of sea beet (Beta vulgaris ssp. maritima; progenitor of all beet crops) and four additional sugar beet accessions. We identified seven million variant positions in the reference genome, and also large regions of low variability, indicating artificial selection. The sugar beet genome sequence enables the identification of genes affecting agronomically relevant traits, supports molecular breeding and maximizes the plant's potential in energy biotechnology.

Construction and performance of network codes

El principal objectiu d'aquest treball és implementar i exposar una descripció teòrica per a diferents esquemes de Physical Layer Network Coding. Utilitzant un esquema bàsic com a punt de partida, el projecte presenta la construcció i l'anàlisis de diferents esquemes de comunicació on la complexitat va augmentant a mesura que anem avançant en el projecte. El treball està estructurat en diferents parts: primer, es presenta una introducció a Physical Layer Network Coding i a Lattice Network Codes. A continuació, s'introdueixen les eines matemàtiques necessàries per entendre el CF System. Després, s'analitza i implementa el primer esquema bàsic. A partir del qual, implementem una versió vectorial del CF System i una versió codificada amb un Hamming q-ari. Finalment, s'estudien i implementen diferents estratègies per millorar la matriu de coeficients A.

Source separation techniques applied to linear prediction

The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach.

Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.