98 resultados para Metabolic Networks and Pathways


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In this paper we consider the structure of dynamically evolving networks modelling information and activity moving across a large set of vertices. We adopt the communicability concept that generalizes that of centrality which is defined for static networks. We define the primary network structure within the whole as comprising of the most influential vertices (both as senders and receivers of dynamically sequenced activity). We present a methodology based on successive vertex knockouts, up to a very small fraction of the whole primary network,that can characterize the nature of the primary network as being either relatively robust and lattice-like (with redundancies built in) or relatively fragile and tree-like (with sensitivities and few redundancies). We apply these ideas to the analysis of evolving networks derived from fMRI scans of resting human brains. We show that the estimation of performance parameters via the structure tests of the corresponding primary networks is subject to less variability than that observed across a very large population of such scans. Hence the differences within the population are significant.

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The intestinal fatty acid-binding protein gene is proposed as a candidate gene for diabetes because the protein it codes is involved in fatty acid absorption and metabolism. This study investigates the association of the Ala54Thr variant of the intestinal fatty acid-binding protein gene on type 2 diabetes mellitus and other related metabolic traits in Asian Indians. Ala54Thr polymorphism was genotyped by using polymerase chain reaction-restriction fragment length polymorphism in unrelated 773 type 2 diabetic and 899 normal glucose-tolerant (NGT) subjects, randomly chosen from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in South India. The Ala54Thr polymorphism was not associated with type 2 diabetes mellitus or obesity. However, genotype-phenotype study revealed that the NGT subjects carrying the Thr54 allele had significantly higher 2-hour plasma glucose (P = .007), glycated hemoglobin (P = .004), 2-hour insulin (P = .027), and fasting low-density lipoprotein cholesterol (P = .032) levels compared with those with the Ala54 allele. Normal glucose-tolerant subjects with Ala54Thr and Thr54Thr genotypes had significantly higher fasting serum triglyceride levels (P = .003) compared with those with Ala54Ala. The subjects were stratified into those with hypertriglyceridemia (serum triglyceride levels >or=150 mg/dL) and those without. The odds ratio for hypertriglyceridemia for the individuals carrying the Ala54Thr genotype was 1.491 (95% confidence interval [CI], 1.22-1.83, P < .0001), and for those carrying the Thr54Thr genotype, it was 1.888 (95% CI, 1.34-2.67; P < .0001). Subjects were also stratified into those with metabolic syndrome (MS) and those without, according to modified Adult Treatment Panel III guidelines. The odds ratio (adjusted for age and sex) for MS for the individuals carrying the Ala54Thr genotype was 1.240 (95% CI, 1.02-1.51; P = .03), whereas for those carrying the Thr54Thr genotype, it was 1.812 (95% CI, 1.28-2.57; P = .001). Carriers of the Thr54 allele have associations with MS and hypertriglyceridemia in this urban South Indian population.

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There are a range of studies based in the low carbon arena which use various ‘futures’- based techniques as ways of exploring uncertainties. These techniques range from ‘scenarios’ and ‘roadmaps’ through to ‘transitions’ andpathways’ as well as ‘vision’-based techniques. The overall aim of the paper is therefore to compare and contrast these techniques to develop a simple working typology with the further objective of identifying the implications of this analysis for RETROFIT 2050. Using recent examples of city-based and energy-based studies throughout, the paper compares and contrasts these techniques and finds that the distinctions between them have often been blurred in the field of low carbon. Visions, for example, have been used in both transition theory and futures/Foresight methods, and scenarios have also been used in transition-based studies as well as futures/Foresight studies. Moreover, Foresight techniques which capture expert knowledge and map existing knowledge to develop a set of scenarios and roadmaps which can inform the development of transitions and pathways can not only help potentially overcome any ‘disconnections’ that may exist between the social and the technical lenses in which such future trajectories are mapped, but also promote a strong ‘co-evolutionary’ content.

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The metabolic syndrome may have its origins in thriftiness, insulin resistance and one of the most ancient of all signalling systems, redox. Thriftiness results from an evolutionarily-driven propensity to minimise energy expenditure. This has to be balanced with the need to resist the oxidative stress from cellular signalling and pathogen resistance, giving rise to something we call 'redox-thriftiness'. This is based on the notion that mitochondria may be able to both amplify membrane-derived redox growth signals as well as negatively regulate them, resulting in an increased ATP/ROS ratio. We suggest that 'redox-thriftiness' leads to insulin resistance, which has the effect of both protecting the individual cell from excessive growth/inflammatory stress, while ensuring energy is channelled to the brain, the immune system, and for storage. We also suggest that fine tuning of redox-thriftiness is achieved by hormetic (mild stress) signals that stimulate mitochondrial biogenesis and resistance to oxidative stress, which improves metabolic flexibility. However, in a non-hormetic environment with excessive calories, the protective nature of this system may lead to escalating insulin resistance and rising oxidative stress due to metabolic inflexibility and mitochondrial overload. Thus, the mitochondrially-associated resistance to oxidative stress (and metabolic flexibility) may determine insulin resistance. Genetically and environmentally determined mitochondrial function may define a 'tipping point' where protective insulin resistance tips over to inflammatory insulin resistance. Many hormetic factors may induce mild mitochondrial stress and biogenesis, including exercise, fasting, temperature extremes, unsaturated fats, polyphenols, alcohol, and even metformin and statins. Without hormesis, a proposed redox-thriftiness tipping point might lead to a feed forward insulin resistance cycle in the presence of excess calories. We therefore suggest that as oxidative stress determines functional longevity, a rather more descriptive term for the metabolic syndrome is the 'lifestyle-induced metabolic inflexibility and accelerated ageing syndrome'. Ultimately, thriftiness is good for us as long as we have hormetic stimuli; unfortunately, mankind is attempting to remove all hormetic (stressful) stimuli from his environment.

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almonella enterica serovar Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of Salmonella Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in energy demand, while growing in glucose minimal medium. By grouping reactions with similar flux responses, a sub-network of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions, that when removed from the genome-scale model interfered with energy and biomass generation. 11 such sets were found to be essential for the production of biomass precursors. Experimental investigation of 7 of these showed that knock-outs of the associated genes resulted in attenuated growth for 4 pairs of reactions, while 3 single reactions were shown to be essential for growth.

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This paper seeks to examine the particular operations of gender and cultural politics that both shaped and restrained possible 'networked' interactions between Jamaican women and their British 'motherlands' during the first forty years of the twentieth century. Paying particular attention to the poetry of Albinia Catherine MacKay (a Scots Creole) and the political journalism of Una Marson (a black Jamaica), I shall seek to examine why both writers speak in and of voices out of place. MacKay's poems work against the critical pull of transnational modernism to reveal aesthetic and cultural isolation through a model of strained belonging in relation to both her Jamaica home and an ancestral Scotland. A small number of poems from her 1912 collection that are dedicated to the historical struggle between the English and Scots for the rule of Scotland and cultural self-determination, some of which are written in a Scottish idiom, may help us to read the complex cultural negotiations that silently inform the seemingly in commensurability of location and locution revealed in these works. In contrast, Marson's journalism, although less known even than her creative writings, is both politically and intellectually radical in its arguments concerning the mutual articulation of race and gender empowerment. However, Marson remains aware of her inability to articulate these convictions with force in a British context and thereby of the way in which speaking out of place also silences her.

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Parasitic infections cause a myriad of responses in their mammalian hosts, on immune as well as on metabolic level. A multiplex panel of cytokines and metabolites derived from four parasite-rodent models, namely, Plasmodium berghei-mouse, Trypanosoma brucei brucei-mouse, Schistosoma mansoni-mouse, and Fasciola hepatica-rat were statistically coanalyzed. 1H NMR spectroscopy and multivariate statistical analysis were used to characterize the urine and plasma metabolite profiles in infected and noninfected animals. Each parasite generated a unique metabolic signature in the host. Plasma cytokine concentrations were obtained using the ‘Meso Scale Discovery’ multi cytokine assay platform. Multivariate data integration methods were subsequently used to elucidate the component of the metabolic signature which is associated with inflammation and to determine specific metabolic correlates with parasite-induced changes in plasma cytokine levels. For example, the relative levels of acetyl glycoproteins extracted from the plasma metabolite profile in the P. berghei-infected mice were statistically correlated with IFN-γ, whereas the same cytokine was anticorrelated with glucose levels. Both the metabolic and the cytokine data showed a similar spatial distribution in principal component analysis scores plots constructed for the combined murine data, with samples from all infected animals clustering according to the parasite species and whereby the protozoan infections (P. berghei and T. b. brucei) grouped separately from the helminth infection (S. mansoni). For S. mansoni, the main infection-responsive cytokines were IL-4 and IL-5, which covaried with lactate, choline, and D-3-hydroxybutyrate. This study demonstrates that the inherently differential immune response to single and multicellular parasites not only manifests in the cytokine expression, but also consequently imprints on the metabolic signature, and calls for in-depth analysis to further explore direct links between immune features and biochemical pathways.

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The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques.

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Obesity and overweight are linked with a cluster of metabolic and vascular disorders that have been termed the metabolic syndrome. Although there is not yet a universally-accepted set of diagnostic criteria, most expert groups agree that the syndrome is characterised by impaired insulin sensitivity and hyperglycaemia, dyslipidaemia (elevated blood triacyglycerols with depressed HDL-cholesterol), abdominal obesity and hypertension. Based on existing published criteria estimates suggest that the syndrome affects a substantial percentage of the middle-aged and elderly populations of most European countries (10-20%) and confers increased risk of type 2 diabetes (2-8(.)8-fold) and CVD (1(.)5-6-fold), as well as having a marked effect on morbidity. Although the pathophysiology is incompletely understood, insulin resistance and abdominal obesity are central to subsequent abnormalities in circulating glucose and lipoproteins, and vascular function that lead to type 2 diabetes, atherosclerosis and CVD. The link between metabolic syndrome, type 2 diabetes and CVD, as well as inability to reverse the present rising rates of obesity, will lead to economically-unsustainable costs of health care in the next 10-20 years. Preventative strategies for metabolic syndrome are required to slow rates of progression and to reduce dependence on costly medical management. A notable development is recent evidence that shows that diet and exercise are more effective than drug treatment in preventing the development of type-2 diabetes in high-risk individuals. The LIPGENE project will investigate dietary fat quality as a strategy for the prevention of metabolic syndrome and identify food chain approaches that can support consumer attempts to alter their dietary patterns.

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Driven by a range of modern applications that includes telecommunications, e-business and on-line social interaction, recent ideas in complex networks can be extended to the case of time-varying connectivity. Here we propose a general frame- work for modelling and simulating such dynamic networks, and we explain how the long time behaviour may reveal important information about the mechanisms underlying the evolution.

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The scaling of metabolic rates to body size is widely considered to be of great biological and ecological importance, and much attention has been devoted to determining its theoretical and empirical value. Most debate centers on whether the underlying power law describing metabolic rates is 2/3 (as predicted by scaling of surface area/volume relationships) or 3/4 ("Kleiber's law"). Although recent evidence suggests that empirically derived exponents vary among clades with radically different metabolic strategies, such as ectotherms and endotherms, models, such as the metabolic theory of ecology, depend on the assumption that there is at least a predominant, if not universal, metabolic scaling exponent. Most analyses claimed to support the predictions of general models, however, failed to control for phylogeny. We used phylogenetic generalized least-squares models to estimate allometric slopes for both basal metabolic rate (BMR) and field metabolic rate (FMR) in mammals. Metabolic rate scaling conformed to no single theoretical prediction, but varied significantly among phylogenetic lineages. In some lineages we found a 3/4 exponent, in others a 2/3 exponent, and in yet others exponents differed significantly from both theoretical values. Analysis of the phylogenetic signal in the data indicated that the assumptions of neither species-level analysis nor independent contrasts were met. Analyses that assumed no phylogenetic signal in the data (species-level analysis) or a strong phylogenetic signal (independent contrasts), therefore, returned estimates of allometric slopes that were erroneous in 30% and 50% of cases, respectively. Hence, quantitative estimation of the phylogenetic signal is essential for determining scaling exponents. The lack of evidence for a predominant scaling exponent in these analyses suggests that general models of metabolic scaling, and macro-ecological theories that depend on them, have little explanatory power.

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This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

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This paper explores the impacts of the HIV/AIDS epidemic on children and families in northern Tanzania using the concept of social resilience.1 The study is based on the findings of childfocused research with street children and children and families from HIV/AIDS-affected households. The paper illustrates the coping strategies that children and young people, and parents and caregivers adopt at the household level. In particular, it examines how the burden of care affects different generations of women and highlights their resilience, together with the importance of social networks and the fluidity of movement between rural and urban areas. The research suggests that migrating to urban areas to seek a living in the informal sector represents a survival strategy adopted by some children and young people orphaned by AIDS when their families and communities are unable or unwilling to support them. The paper concludes by exploring parents’, caregivers’, children’s, and young people’s views on the forms of social support that would promote their resilience and thereby help to mitigate the impacts of the epidemic at the household level.