El comercio internacional: la cuestión apícola en la OMC.

Es propósito de este trabajo analizar el rol de las Organizaciones Internacionales de Comercio y el papel que juegan en la resolución de los conflictos originados en la puja de los países no industrializados por acceder a los mercados de la manera más beneficiosa posible frente a las políticas proteccionistas impuestas, casualmente, por aquellos que abogan por el libre comercio y la apertura irrestricta. Haciendo hincapié en la OMC se analizará su funcionamiento a través de un estudio de caso

A Semantic-Based Framework for Summarization and Page Segmentation in Web Mining

This chapter addresses two crucial issues that arise when one applies Web-mining techniques for extracting relevant information. The first one is the acquisition of useful knowledge from textual data; the second issue stems from the fact that a web page often proposes a considerable amount of \u2018noise\u2019 with respect to the sections that are truly informative for the user's purposes. The novelty contribution of this work lies in a framework that can tackle both these tasks at the same time, supporting text summarization and page segmentation. The approach achieves this goal by exploiting semantic networks to map natural language into an abstract representation, which eventually supports the identification of the topics addressed in a text source. A heuristic algorithm uses the abstract representation to highlight the relevant segments of text in the original document. The verification of the approach effectiveness involved a publicly available benchmark, the DUC 2002 dataset, and satisfactory results confirmed the method effectiveness

An approximate randomization-based neural network with dedicated digital architecture for energy-constrained devices

Variable energy constraints affect the implementations of neural networks on battery-operated embedded systems. This paper describes a learning algorithm for randomization-based neural networks with hard-limit activation functions. The approach adopts a novel cost function that balances accuracy and network complexity during training. From an energyspecific perspective, the new learning strategy allows to adjust, dynamically and in real time, the number of operations during the network’s forward phase. The proposed learning scheme leads to efficient predictors supported by digital architectures. The resulting digital architecture can switch to approximate computing at run time, in compliance with the available energy budget. Experiments on 10 real-world prediction testbeds confirmed the effectiveness of the learning scheme. Additional tests on limited-resource devices supported the implementation efficiency of the overall design approac

Bax/Bak promote sumoylation of DRP1 and its stable association with mitochondria during apoptotic cell death

Dynamin-related protein 1 (DRP1) plays an important role in mitochondrial fission at steady state and during apoptosis. Using fluorescence recovery after photobleaching, we demonstrate that in healthy cells, yellow fluorescent protein (YFP)–DRP1 recycles between the cytoplasm and mitochondria with a half-time of 50 s. Strikingly, during apoptotic cell death, YFP-DRP1 undergoes a transition from rapid recycling to stable membrane association. The rapid cycling phase that characterizes the early stages of apoptosis is independent of Bax/Bak. However, after Bax recruitment to the mitochondrial membranes but before the loss of mitochondrial membrane potential, YFP-DRP1 becomes locked on the membrane, resulting in undetectable fluorescence recovery. This second phase in DRP1 cycling is dependent on the presence of Bax/Bak but independent of hFis1 and mitochondrial fragmentation. Coincident with Bax activation, we detect a Bax/Bak-dependent stimulation of small ubiquitin-like modifier-1 conjugation to DRP1, a modification that correlates with the stable association of DRP1 with mitochondrial membranes. Altogether, these data demonstrate that the apoptotic machinery regulates the biochemical properties of DRP1 during cell death

Vps35 Mediates Vesicle Transport between the Mitochondria and Peroxisomes

SummaryMitochondria-derived vesicles (MDVs) have been shown to transport cargo from the mitochondria to the peroxisomes [1]. Mitochondria and peroxisomes share common functions in the oxidation of fatty acids and the reduction of damaging peroxides [2, 3]. Their biogenesis is also linked through both the activation of master transcription factors such as PGC-1α [4, 5] and the common use of fission machinery, including DRP1, Mff, and hFis1 [6–9]. We have previously shown that MDVs are formed independently of the known mitochondrial fission GTPase Drp1 and are enriched for a mitochondrial small ubiquitin-like modifier (SUMO) E3 ligase called MAPL (mitochondrial-anchored protein ligase) [1]. Here, we demonstrate that the retromer complex, a known component of vesicle transport from the endosome to the Golgi apparatus [10–13], regulates the transport of MAPL from mitochondria to peroxisomes. An unbiased screen shows that Vps35 and Vps26 are found in complex with MAPL, and confocal imaging reveals Vps35 recruitment to mitochondrial vesicles. Silencing of Vps35 or Vps26A leads to a significant reduction in the delivery of MAPL to peroxisomes, placing the retromer within a novel intracellular trafficking route and providing insight into the formation of MAPL-positive MDVs

Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08

The International Workshop on Computational Intelligence for Security in Information Systems (CISIS) proposes a meeting ground to the various communities involved in building intelligent systems for security, namely: information security, data mining, adaptive learning methods and soft computing among others. The main goal is to allow experts and researchers to assess the benefits of learning methods in the data-mining area for information-security applications. The Workshop offers the opportunity to interact with the leading industries actively involved in the critical area of security, and have a picture of the current solutions adopted in practical domains. This volume of Advances in Soft Computing contains accepted papers presented at CISIS’08, which was held in Genova, Italy, on October 23rd-24th, 2008. The selection process to set up the Workshop program yielded a collection of about 40 papers. This allowed the Scientific Committee to verify the vital and crucial nature of the topics involved in the event, and resulted in an acceptance rate of about 60% of the originally submitted manuscripts

Neural projection techniques for the visual inspection of network traffic

A crucial aspect in network monitoring for security purposes is the visual inspection of the traffic pattern, mainly aimed to provide the network manager with a synthetic and intuitive representation of the current situation. Towards that end, neural projection techniques can map high-dimensional data into a low-dimensional space adaptively, for the user-friendly visualization of monitored network traffic. This work proposes two projection methods, namely, cooperative maximum likelihood Hebbian learning and auto-associative back-propagation networks, for the visual inspection of network traffic. This set of methods may be seen as a complementary tool in network security as it allows the visual inspection and comprehension of the traffic data internal structure. The proposed methods have been evaluated in two complementary and practical network-security scenarios: the on-line processing of network traffic at packet level, and the off-line processing of connection records, e.g. for post-mortem analysis or batch investigation. The empirical verification of the projection methods involved two experimental domains derived from the standard corpora for evaluation of computer network intrusion detection: the MIT Lincoln Laboratory DARPA dataset

Golgi derived PI(4)P-containing vesicle drives late steps of mitochondrial division

Mitochondrial plasticity is a key regulator of cell fate decisions. Mitochondrial division involves Dynamin-related protein-1 (Drp1) oligomerization, which constricts membranes at endoplasmic reticulum (ER) contact sites. The mechanisms driving the final steps of mitochondrial division are still unclear. Here, we found that microdomains of phosphatidylinositol 4-phosphate (PI(4)P) on Trans-Golgi network (TGN) vesicles were recruited to mitochondria-ER contact sites and could drive mitochondrial division downstream of Drp1. The loss of the small GTPase ADP-ribosylation factor 1 (Arf1) or its effector, phosphatidylinositol 4-kinase IIIβ (PI(4)KIIIβ) in different mammalian cell lines, prevented PI(4)P generation and led to a hyperfused and branched mitochondrial network, marked with extended mitochondrial constriction sites. Thus, recruitment of TGN-PI(4)P-containing vesicles at mitochondria-ER contact sites may trigger final events leading to mitochondrial scission.This work was supported by the Canadian Institutes of Health Research Operating Grants Program (CIHR grant 68833 to H.M.M.), the Medical Research Council (MRC grants MC_UU_00015/7 and RG89175 to J.P.), the Isaac Newton Trust (grant RG89529 to J.P.), and the Wellcome Trust Institutional Strategic Support Fund (grant RG89305 to J.P.). H.M.M. is a Canada Research Chair. S.N. and L.-C.T. are recipients of Daiichi Sankyo Foundation of Life Science and Ramon Areces postdoctoral fellowships, respectively. L.T. was supported by an MRC-funded graduate student fellowship. V.P was supported by the European Union’s Horizon 2020 research and innovation program (MITODYN-749926)

Auto-Associative Neural Techniques for Intrusion Detection Systems

Intrusion detection systems (IDS's) ensure the security of computer networks by monitoring traffic and generating alerts, or taking actions, when suspicious activities are detected. This paper proposes a network-based IDS supporting an intuitive visualization of the time evolution of network traffic. The system is designed to assist the network manager in detecting anomalies, and exploits auto-associative back-propagation (AABP) neural networks to turn raw data extracted from traffic sources into an intuitive 2D representation. The neural component operates as a sort of smart compression operator and supports a compact representation of multi-dimensional data. The empirical verification of the mapping method involved the detection of anomalies in traffic ascribed to the simple network management protocol (SNMP), and confirmed the validity of the proposed approach

Computational-Intelligence Models for Visualization-based Intrusion Detection Systems.

Intrusion Detection Systems (IDS’s) are essential components in a network communication infrastructure, as they enforce security by monitoring traffic and detecting malicious activities. In this research, Computational Intelligence models support an IDS technology to obtain a synthetic, effective visualization of the traffic analysis. Auto-Associative Back-Propagation (AABP) neural networks map feature vectors extracted from traffic sources into a compact representation on a 2-D display. During training, the neural network learns to compress the data in an unsupervised fashion; at run time, the trained neural component synthesizes an effective, 2-D representation of the traffic situation. Empirical tests involving Simple Network Management Protocol (SNMP) traffic proved the validity of the approach.