Scattering poles near the real axis for two strictly convex obstacles

Iantchenko, A., (2007) 'Scattering poles near the real axis for two strictly convex obstacles', Annales of the Institute Henri Poincar? 8 pp.513-568 RAE2008

Modification of nitrogen remobilisation, grain fill and leaf senescence in maize (Zea mays L.) by transposon insertional mutagenensis in a protease gene

Donnison, I. S., Gay, A. P., Thomas, Howard, Edwards, K. J., Edwards, D., James, C. L., Thomas, A. M., Ougham, H. J. (2007). Modification of nitrogen remobilization, grain fill and leaf senescence in maize (Zea mays) by transposon insertional mutagenensis in a protease gene. New Phytologist, 173 (3), 481-494. Sponsorship: BBSRC RAE2008

Hacia una educación intercultural: diversidad y convivencia en un centro de Pamplona

“Hacia una educación intercultural: diversidad y convivencia en un centro de Pamplona” es un trabajo que hace un recorrido por la sociedad multicultural actual y pretende acercar la experiencia multicultural vivida en un colegio del barrio de San Jorge de Pamplona. Este trabajo se centra en la interculturalidad como realidad social y la educación intercultural como elementos fundamentales para convivir desde la comunicación y el intercambio cultural. Ante los cambios sociales producidos en España debido a la inmigración, la escuela se ha visto inmersa en un proceso de cambio. La educación intercultural encuentra una respuesta positiva ante esta nueva realidad y, mediante el trabajo, podemos hacernos una idea de cómo se está viviendo este cambio y qué supone para el sistema educativo.

Unia Europejska wobec nowych tworów państwowych (casus Kosowa, Abchazji i Osetii południowej)

W przypadku niejednolitego stanowiska państw członkowskich Unii Europejskiej wobec Kosowa, osłabieniu ulega prestiż i siła oddziaływania Unii na arenie międzynarodowej. W odniesieniu do problemu Abchazji i Osetii Południowej natomiast, działania UE obrazują, że samo wyrażenie zainteresowania określonym problemem oraz udzielanie ‘apolitycznej’ pomocy finansowej, którym nie towarzyszą wyraźnie i precyzyjnie zdefiniowane cele polityczne, działalność instytucji wyposażonych w silny mandat oraz proces aktywnych negocjacji, nie przyczynia się do rozwiązania palącego problemu międzynarodowego, narażając na szwank pozycję międzynarodową Unii Europejskiej. Po raz kolejny państwa członkowskie pokazują, iż koncepcja Wspólnej Polityki Zagranicznej i Bezpieczeństwa, wspierana między innymi przez Europejską Politykę Sąsiedztwa, jest pozbawiona stabilnych podstaw.

Geomorfologiczne uwarunkowania rozwoju sieci komunikacyjnej na obszarze młodoglacjalnym (zlewnia Perznicy, Pojezierze Drawskie

The aim of the studies in the Perznica River catchment were relief changes caused by the development of transportation infrastructure. This type of transformation is dependable on the state of economy and the settlements. The development of transportation network in the last two hundred years was examined through the analysis of archival cartographic materials – maps from the years 1789, 1855, 1877, 1935 – and the comparison with the situation from mid 1980s. The Perznica River catchment has an area of 249 km2 and it is located in north-western Poland in the central part of the Drawskie Lakeland macroregion, which belongs to the West Pomeranian Lakeland. The heterogeneous Perznica River catchment relief has a denivelation of 159 m and is within 60 and 219 m a.s.l. The study area is within the Parsęta River lobe. A number of subzones, whose morphological diversity and diversity of sediments lithofacies is mainly a reflection of areal deglaciation of the continental ice-sheet marginal zone, has been distinguished and these are: • subzone of the internal kame moraine – the undulated moraine upland, diversified by kame forms and kettle holes, • subzone of ice-free space forms – the uplands of kame plateaux, • subzone of melt-out lake basins – Lake Wielatowo basin with a characteristic collar ridge, • morphological levels of the northern Pomeranian sloping surface – mainly flat moraine uplands and small outwashes. The economic development of the Perznica River catchment advanced in close connection with the physical and geographical context, mainly with the relief, soils and hydrological conditions. As a result, the flat moraine uplands and marginal outflow plains, which were easiest to cultivate, have been developed and populated faster than any other. Since the early medieval period, large, compact villages, often centered around big estates, were emerging in those areas. In areas with a high relief energy–kame-melt moraines, ice-free space forms and ridges around melt-out lake basins–farming entered on a larger scale from the eighteenth century. Scattered settlements in those areas forced the creation of a dense access road network to farms and fields. In the case of anthropogenic forms of transportation with denivelation exceeding 1 m in the study area, road excavations are present for 37.3 km, road undercuttings for 43.8 km and road embankments for 38.7 km in total length. That gives a high ratio of density of such forms, equal to 2.1 km per km–2.

Asesoría en diseño.

106 hojas : ilustraciones.

Programa Nacional de Joyería. Asesoría en Diseño. Bogotá, 2001 - 2002.

113 hojas : ilustraciones.

Programa Nacional de Joyería. Taller de Diseño, Departamento de Antioquia municipios de Frontino y Santa Fe de Antioquia febrero 19 a marzo 17 de 2002.

107 hojas : Ilustraciones.

Promoción de las artesanías en el Reinado Nacional de Belleza 1999.

Documento de diez fotografías a color.

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

DIP: Density Inference Protocol for wireless sensor networks and its application to density-unbiased statistics

Wireless sensor networks have recently emerged as enablers of important applications such as environmental, chemical and nuclear sensing systems. Such applications have sophisticated spatial-temporal semantics that set them aside from traditional wireless networks. For example, the computation of temperature averaged over the sensor field must take into account local densities. This is crucial since otherwise the estimated average temperature can be biased by over-sampling areas where a lot more sensors exist. Thus, we envision that a fundamental service that a wireless sensor network should provide is that of estimating local densities. In this paper, we propose a lightweight probabilistic density inference protocol, we call DIP, which allows each sensor node to implicitly estimate its neighborhood size without the explicit exchange of node identifiers as in existing density discovery schemes. The theoretical basis of DIP is a probabilistic analysis which gives the relationship between the number of sensor nodes contending in the neighborhood of a node and the level of contention measured by that node. Extensive simulations confirm the premise of DIP: it can provide statistically reliable and accurate estimates of local density at a very low energy cost and constant running time. We demonstrate how applications could be built on top of our DIP-based service by computing density-unbiased statistics from estimated local densities.

Small-World Internet Topologies: Possible Causes and Implications on Scalability of End-System Multicast

Recent work has shown the prevalence of small-world phenomena [28] in many networks. Small-world graphs exhibit a high degree of clustering, yet have typically short path lengths between arbitrary vertices. Internet AS-level graphs have been shown to exhibit small-world behaviors [9]. In this paper, we show that both Internet AS-level and router-level graphs exhibit small-world behavior. We attribute such behavior to two possible causes–namely the high variability of vertex degree distributions (which were found to follow approximately a power law [15]) and the preference of vertices to have local connections. We show that both factors contribute with different relative degrees to the small-world behavior of AS-level and router-level topologies. Our findings underscore the inefficacy of the Barabasi-Albert model [6] in explaining the growth process of the Internet, and provide a basis for more promising approaches to the development of Internet topology generators. We present such a generator and show the resemblance of the synthetic graphs it generates to real Internet AS-level and router-level graphs. Using these graphs, we have examined how small-world behaviors affect the scalability of end-system multicast. Our findings indicate that lower variability of vertex degree and stronger preference for local connectivity in small-world graphs results in slower network neighborhood expansion, and in longer average path length between two arbitrary vertices, which in turn results in better scaling of end system multicast.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.