Complex roles of inflammasomes in carcinogenesis.

The central role of chronic inflammation in the promotion of tumor growth is supported by a broad range of experimental and clinical evidence. However, the molecular mechanisms converting transient inflammatory tissue reactions into a tumor-promoting microenvironment remain largely elusive. Because inflammasomes have been shown to regulate the proinflammatory cytokines interleukin 1β (IL-1β) and IL-18, they have been implicated in the relationship between tumor genesis/progression and inflammation. For instance, many cancers have been directly linked to inflammasome-mediated sterile inflammation, where a blockade of IL-1β and IL-18 has been shown to inhibit tumor growth. On the other hand, inflammasome activation also has potent antitumorigenic effects, where malignant precursor cells are eliminated through pyroptotic cell death. Indeed, inflammasome activity can even increase the efficacy of certain chemotherapies. Here, we review the current understanding on the complex and sometimes contradictory role of inflammasomes in carcinogenesis.

From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways

Background: Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks. Results: First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented. Conclusion: In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.

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.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

Mutual information of IID complex Gaussian signals on block Rayleigh-faded channels

We present a method to compute, quickly and efficiently, the mutual information achieved by an IID (independent identically distributed) complex Gaussian signal on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and MIMO (multiple-input multiple-output) settings. Operationally, this mutual information represents the highest spectral efficiency that can be attained using Gaussiancodebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified when multiple transmit antennas are used. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.

Mutual Information of IID complex Gaussian signals on block Rayleigh-faded channels

We present a method to compute, quickly and efficiently, the mutual information achieved by an IID (independent identically distributed) complex Gaussian signal on a block Rayleigh-faded channel without side information at the receiver. The method accommodates both scalar and MIMO (multiple-input multiple-output) settings. Operationally, this mutual information represents the highest spectral efficiency that can be attained using Gaussiancodebooks. Examples are provided that illustrate the loss in spectral efficiency caused by fast fading and how that loss is amplified when multiple transmit antennas are used. These examples are further enriched by comparisons with the channel capacity under perfect channel-state information at the receiver, and with the spectral efficiency attained by pilot-based transmission.

Parallel Research, Multiple Intellectual Property Right Protection Instruments, and the Correlation among R&D Projects, September 2005

The choice of a research path in attacking scientific and technological problems is a significant component of firms’ R&D strategy. One of the findings of the patent races literature is that, in a competitive market setting, firms’ noncooperative choices of research projects display an excessive degree of correlation, as compared to the socially optimal level. The paper revisits this question in a context in which firms have access to trade secrets, in addition to patents, to assert intellectual property rights (IPR) over their discoveries. We find that the availability of multiple IPR protection instruments can move the paths chosen by firms engaged in an R&D race toward the social optimum.

Genetik der essentiellen arteriellen Hypertonie [Essential hypertension - which genes can be held responsible for it?].

Hypertension is a common heritable cardiovascular risk factor. Some rare monogenic forms of hypertension have been described, but the majority of patients suffer from essential hypertension, for whom the underlying genetic mechanisms are not clear. Essential hypertension is a complex trait, involving multiple genes and environmental factors. Recently, progress in the identification of common genetic variants associated with essential hypertension has been made due to large-scale international collaborative projects. In this article we review the new research methods used as well as selected recent findings in this field.

Swain: Stata module to correct the SEM chi-square overidentification test in small sample sizes or complex models. Statistical Software Components S457617, Boston College Department of Economics.

Swain corrects the chi-square overidentification test (i.e., likelihood ratio test of fit) for structural equation models whethr with or without latent variables. The chi-square statistic is asymptotically correct; however, it does not behave as expected in small samples and/or when the model is complex (cf. Herzog, Boomsma, & Reinecke, 2007). Thus, particularly in situations where the ratio of sample size (n) to the number of parameters estimated (p) is relatively small (i.e., the p to n ratio is large), the chi-square test will tend to overreject correctly specified models. To obtain a closer approximation to the distribution of the chi-square statistic, Swain (1975) developed a correction; this scaling factor, which converges to 1 asymptotically, is multiplied with the chi-square statistic. The correction better approximates the chi-square distribution resulting in more appropriate Type 1 reject error rates (see Herzog & Boomsma, 2009; Herzog, et al., 2007).

Noms que formen part d'un determinant complex

Aquest article examina un aspecte de la informació gramatical que inclouen els diccionaris. En concret, analitza el tractament lexicogràfic que els noms que poden formar part d'un determinant complex han rebut en diversos diccionaris. Són noms que, segons els contextos funcionen com a nucli d'un sintagma nominal o com a nucli d'un sintagma determinant. Els resultats d'aquest estudi demostren que la informació gramatical en aquest tipus de noms en la majoria de diccionaris és molt pobre i fins i tot nul·la. Com a alternativa, el treball proposa un primer disseny d'entrada lexicogràfica prototípica per aquest tipus de noms que al costat de la informació semàntica té en compte la informació gramatical i la informació pragmàtica.

The SM protein Sly1 accelerates assembly of the ER-Golgi SNARE complex.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) and Sec1/Munc18 (SM) proteins constitute the core of an ancient vesicle fusion machine that diversified into distinct sets that now function in different trafficking steps in eukaryotic cells. Deciphering their precise mode of action has proved challenging. SM proteins are thought to act primarily through one type of SNARE protein, the syntaxins. Despite high structural similarity, however, contrasting binding modes have been found for different SM proteins and syntaxins. Whereas the secretory SM protein Munc18 binds to the ‟closed conformation" of syntaxin 1, the ER-Golgi SM protein Sly1 interacts only with the N-peptide of Sed5. Recent findings, however, indicate that SM proteins might interact simultaneously with both syntaxin regions. In search for a common mechanism, we now reinvestigated the Sly1/Sed5 interaction. We found that individual Sed5 adopts a tight closed conformation. Sly1 binds to both the closed conformation and the N-peptide of Sed5, suggesting that this is the original binding mode of SM proteins and syntaxins. In contrast to Munc18, however, Sly1 facilitates SNARE complex formation by loosening the closed conformation of Sed5.

Alpine tectonic evolution of a Jurassic subduction-accretionary complex: Deformation, kinematics and 40Ar/39Ar age constraints on the Mesozoic low-grade schists of the Circum-Rhodope Belt in the eastern Rhodope-Thrace region, Bulgaria-Greece

Deformation of the Circum-Rhodope Belt Mesozoic (Middle Triassic to earliest Lower Cretaceous) low-grade schists underneath an arc-related ophiolitic magmatic suite and associated sedimentary successions in the eastern Rhodope-Thrace region occurred as a two-episode tectonic process: (i) Late Jurassic deformation of arc to margin units resulting from the eastern Rhodope-Evros arc-Rhodope terrane continental margin collision and accretion to that margin, and (ii) Middle Eocene deformation related to the Tertiary crustal extension and final collision resulting in the closure of the Vardar ocean south of the Rhodope terrane. The first deformational event D-1 is expressed by Late Jurassic NW-N vergent fold generations and the main and subsidiary planar-linear structures. Although overprinting, these structural elements depict uniform bulk north-directed thrust kinematics and are geometrically compatible with the increments of progressive deformation that develops in same greenschist-facies metamorphic grade. It followed the Early-Middle Jurassic magmatic evolution of the eastern Rhodope-Evros arc established on the upper plate of the southward subducting Maliac-Meliata oceanic lithosphere that established the Vardar Ocean in a supra-subduction back-arc setting. This first event resulted in the thrust-related tectonic emplacement of the Mesozoic schists in a supra-crustal level onto the Rhodope continental margin. This Late Jurassic-Early Cretaceous tectonic event related to N-vergent Balkan orogeny is well-constrained by geochronological data and traced at a regional-scale within distinct units of the Carpatho-Balkan Belt. Following subduction reversal towards the north whereby the Vardar Ocean was subducted beneath the Rhodope margin by latest Cretaceous times, the low-grade schists aquired a new position in the upper plate, and hence, the Mesozoic schists are lacking the Cretaceous S-directed tectono-metamorphic episode whose effects are widespread in the underlying high-grade basement. The subduction of the remnant Vardar Ocean located behind the colliding arc since the middle Cretaceous was responsible for its ultimate closure, Early Tertiary collision with the Pelagonian block and extension in the region caused the extensional collapse related to the second deformational event D-2. This extensional episode was experienced passively by the Mesozoic schists located in the hanging wall of the extensional detachments in Eocene times. It resulted in NE-SW oriented open folds representing corrugation antiforms of the extensional detachment surfaces, brittle faulting and burial history beneath thick Eocene sediments as indicated by 42.1-39.7 Ma Ar-40/Ar-39 mica plateau ages obtained in the study. The results provide structural constraints for the involvement components of Jurassic paleo-subduction zone in a Late Jurassic arc-continental margin collisional history that contributed to accretion-related crustal growth of the Rhodope terrane. (C) 2011 Elsevier Ltd. All rights reserved.

The A-kinase anchoring protein (AKAP)-Lbc-signaling complex mediates alpha1 adrenergic receptor-induced cardiomyocyte hypertrophy.

In response to various pathological stresses, the heart undergoes a pathological remodeling process that is associated with cardiomyocyte hypertrophy. Because cardiac hypertrophy can progress to heart failure, a major cause of lethality worldwide, the intracellular signaling pathways that control cardiomyocyte growth have been the subject of intensive investigation. It has been known for more than a decade that the small molecular weight GTPase RhoA is involved in the signaling pathways leading to cardiomyocyte hypertrophy. Although some of the hypertrophic pathways activated by RhoA have now been identified, the identity of the exchange factors that modulate its activity in cardiomyocytes is currently unknown. In this study, we show that AKAP-Lbc, an A-kinase anchoring protein (AKAP) with an intrinsic Rho-specific guanine nucleotide exchange factor activity, is critical for activating RhoA and transducing hypertrophic signals downstream of alpha1-adrenergic receptors (ARs). In particular, our results indicate that suppression of AKAP-Lbc expression by infecting rat neonatal ventricular cardiomyocytes with lentiviruses encoding AKAP-Lbc-specific short hairpin RNAs strongly reduces both alpha1-AR-mediated RhoA activation and hypertrophic responses. Interestingly, alpha1-ARs promote AKAP-Lbc activation via a pathway that requires the alpha subunit of the heterotrimeric G protein G12. These findings identify AKAP-Lbc as the first Rho-guanine nucleotide exchange factor (GEF) involved in the signaling pathways leading to cardiomyocytes hypertrophy.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.