DED or alive: assembly and regulation of the death effector domain complexes.

Death effector domains (DEDs) are protein-protein interaction domains initially identified in proteins such as FADD, FLIP and caspase-8 involved in regulating apoptosis. Subsequently, these proteins have been shown to have important roles in regulating other forms of cell death, including necroptosis, and in regulating other important cellular processes, including autophagy and inflammation. Moreover, these proteins also have prominent roles in innate and adaptive immunity and during embryonic development. In this article, we review the various roles of DED-containing proteins and discuss recent developments in our understanding of DED complex formation and regulation. We also briefly discuss opportunities to therapeutically target DED complex formation in diseases such as cancer.

Tumor suppressor Ras-association domain family 1 isoform A is a novel regulator of cardiac hypertrophy

BACKGROUND: Ras signaling regulates a number of important processes in the heart, including cell growth and hypertrophy. Although it is known that defective Ras signaling is associated with Noonan, Costello, and other syndromes that are characterized by tumor formation and cardiac hypertrophy, little is known about factors that may control it. Here we investigate the role of Ras effector Ras-association domain family 1 isoform A (RASSF1A) in regulating myocardial hypertrophy.

METHODS AND RESULTS: A significant downregulation of RASSF1A expression was observed in hypertrophic mouse hearts, as well as in failing human hearts. To further investigate the role of RASSF1A in cardiac (patho)physiology, we used RASSF1A knock-out (RASSF1A(-)(/)(-)) mice and neonatal rat cardiomyocytes with adenoviral overexpression of RASSF1A. Ablation of RASSF1A in mice significantly enhanced the hypertrophic response to transverse aortic constriction (64.2% increase in heart weight/body weight ratio in RASSF1A(-)(/)(-) mice compared with 32.4% in wild type). Consistent with the in vivo data, overexpression of RASSF1A in cardiomyocytes markedly reduced the cellular hypertrophic response to phenylephrine stimulation. Analysis of molecular signaling events in isolated cardiomyocytes indicated that RASSF1A inhibited extracellular regulated kinase 1/2 activation, likely by blocking the binding of Raf1 to active Ras.

CONCLUSIONS: Our data establish RASSF1A as a novel inhibitor of cardiac hypertrophy by modulating the extracellular regulated kinase 1/2 pathway.

An empirical polarization domain channel availability model for cognitive radio

In dynamic spectrum access networks, cognitive radio terminals monitor their spectral environment in order to detect and opportunistically access unoccupied frequency channels. The overall performance of such networks depends on the spectrum occupancy or availability patterns. Accurate knowledge on the channel availability enables optimum performance of such networks in terms of spectrum and energy efficiency. This work proposes a novel probabilistic channel availability model that can describe the channel availability in different polarizations for mobile cognitive radio terminals that are likely to change their orientation during their operation. A Gaussian approximation is used to model the empirical occupancy data that was obtained through a measurement campaign in the cellular frequency bands within a realistic operational scenario.

A statistical framework for channel availability modelling in the polarisation domain

Cognitive radio has been proposed as a means of improving the spectrum utilisation and increasing spectrum efficiency of wireless systems. This can be achieved by allowing cognitive radio terminals to monitor their spectral environment and opportunistically access the unoccupied frequency channels. Due to the opportunistic nature of cognitive radio, the overall performance of such networks depends on the spectrum occupancy or availability patterns. Appropriate knowledge on channel availability can optimise the sensing performance in terms of spectrum and energy efficiency. This work proposes a statistical framework for the channel availability in the polarization domain. A Gaussian Normal approximation is used to model real-world occupancy data obtained through a measurement campaign in the cellular frequency bands within a realistic scenario.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

A frequency-domain receiver for asynchronous systems employing CP-assisted DS-CDMA schemes

In this paper we consider the uplink transmission within CP-assisted (Cyclic Pre¯x) DS-CDMA (Direct Sequence Code Division Multiple Access) systems and we present a frequency-domain MUD (MultiUser Detection) receiver with iterative estimation and compensation of residual frequency errors. The proposed receiver is suitable for broadband wireless systems, with performances that can be close to the single-user MFB (Matched Filter Bound), even for fully loaded systems and/or in the presence of strong interfering signals. The receiver is powerful enough for typical asynchronous scenarios, requiring only a coarse synchronization between users.

Frequency- domain multiuser detection for highly overloaded DS-CDMA systems

A DS-CDMA (Direct Sequence-Coded Division Multiple Access) system has maximum spectral efficiency if the system is fully loaded (i.e., the number of users is equal to the spreading factor) and we employ signals with bandwidth equal to the chip rate. However, due to implementation constraints we need to employ signals with higher bandwidth, decreasing the system’s spectral efficiency. In this paper we consider prefixassisted DS-CDMA systems with bandwidth that can be significantly above the chip rate. To allow high spectral efficiency we consider highly overloaded systems where the number of users can be twice the spreading factor or even more. To cope with the strong interference levels we present an iterative frequencydomain receiver that takes full advantage of the total bandwidth of the transmitted signals. Our performance results show that the proposed receiver can have excellent performance, even for highly overloaded systems. Moreover, the overall system performance can be close to the maximum theoretical spectral efficiency, even with transmitted signals that have bandwidth significantly above the chip rate.

Role of Automated Open Source Systems in the Intelligence Analysis of a High-Level Domain Expert

Open-domain web-based multiple document : question answering for list questions with support for temporal restrictors

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2015

The N-terminal domain modulates α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor desensitization

AMPA receptors are tetrameric glutamate-gated ion channels that mediate fast synaptic neurotransmission in mammalian brain. Their subunits contain a two-lobed N-terminal domain (NTD) that comprises over 40% of the mature polypeptide. The NTD is not obligatory for the assembly of tetrameric receptors, and its functional role is still unclear. By analyzing full-length and NTD-deleted GluA1-4 AMPA receptors expressed in HEK 293 cells, we found that the removal of the NTD leads to a significant reduction in receptor transport to the plasma membrane, a higher steady state-to-peak current ratio of glutamate responses, and strongly increased sensitivity to glutamate toxicity in cell culture. Further analyses showed that NTD-deleted receptors display both a slower onset of desensitization and a faster recovery from desensitization of agonist responses. Our results indicate that the NTD promotes the biosynthetic maturation of AMPA receptors and, for membrane-expressed channels, enhances the stability of the desensitized state. Moreover, these findings suggest that interactions of the NTD with extracellular/synaptic ligands may be able to fine-tune AMPA receptor-mediated responses, in analogy with the allosteric regulatory role demonstrated for the NTD of NMDA receptors.

Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Time domain design of fractional differintegrators using least-squares

In this paper we propose the use of the least-squares based methods for obtaining digital rational approximations (IIR filters) to fractional-order integrators and differentiators of type sα, α∈R. Adoption of the Padé, Prony and Shanks techniques is suggested. These techniques are usually applied in the signal modeling of deterministic signals. These methods yield suboptimal solutions to the problem which only requires finding the solution of a set of linear equations. The results reveal that the least-squares approach gives similar or superior approximations in comparison with other widely used methods. Their effectiveness is illustrated, both in the time and frequency domains, as well in the fractional differintegration of some standard time domain functions.

An Extension of Estimation of Domain of Attraction for Fractional Order Linear System Subject to Saturation Control

This paper employs the Lyapunov direct method for the stability analysis of fractional order linear systems subject to input saturation. A new stability condition based on saturation function is adopted for estimating the domain of attraction via ellipsoid approach. To further improve this estimation, the auxiliary feedback is also supported by the concept of stability region. The advantages of the proposed method are twofold: (1) it is straightforward to handle the problem both in analysis and design because of using Lyapunov method, (2) the estimation leads to less conservative results. A numerical example illustrates the feasibility of the proposed method.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.