Risk stratification by pre-operative cardiopulmonary exercise testing improves outcomes following elective abdominal aortic aneurysm surgery : a cohort study

Background: In 2009, the NHS evidence adoption center and National Institute for Health and Care Excellence (NICE) published a review of the use of endovascular aneurysm repair (EVAR) of abdominal aortic aneurysms (AAAs). They recommended the development of a risk-assessment tool to help identify AAA patients with greater or lesser risk of operative mortality and to contribute to mortality prediction. A low anaerobic threshold (AT), which is a reliable, objective measure of pre-operative cardiorespiratory fitness, as determined by pre-operative cardiopulmonary exercise testing (CPET) is associated with poor surgical outcomes for major abdominal surgery. We aimed to assess the impact of a CPET-based risk-stratification strategy upon perioperative mortality, length of stay and non-operative costs for elective (open and endovascular) infra-renal AAA patients. Methods: A retrospective cohort study was undertaken. Pre-operative CPET-based selection for elective surgical intervention was introduced in 2007. An anonymized cohort of 230 consecutive infra-renal AAA patients (2007 to 2011) was studied. A historical control group of 128 consecutive infra-renal AAA patients (2003 to 2007) was identified for comparison. Comparative analysis of demographic and outcome data for CPET-pass (AT ≥ 11 ml/kg/min), CPET-fail (AT < 11 ml/kg/min) and CPET-submaximal (no AT generated) subgroups with control subjects was performed. Primary outcomes included 30-day mortality, survival and length of stay (LOS); secondary outcomes were non-operative inpatient costs. Results: Of 230 subjects, 188 underwent CPET: CPET-pass n = 131, CPET-fail n = 35 and CPET-submaximal n = 22. When compared to the controls, CPET-pass patients exhibited reduced median total LOS (10 vs 13 days for open surgery, n = 74, P < 0.01 and 4 vs 6 days for EVAR, n = 29, P < 0.05), intensive therapy unit requirement (3 vs 4 days for open repair only, P < 0.001), non-operative costs (£5,387 vs £9,634 for open repair, P < 0.001) and perioperative mortality (2.7% vs 12.6% (odds ratio: 0.19) for open repair only, P < 0.05). CPET-stratified (open/endovascular) patients exhibited a mid-term survival benefit (P < 0.05). Conclusion: In this retrospective cohort study, a pre-operative AT > 11 ml/kg/min was associated with reduced perioperative mortality (open cases only), LOS, survival and inpatient costs (open and endovascular repair) for elective infra-renal AAA surgery

Upper atmospheres and ionospheres of planets and satellites

The upper atmospheres of the planets and their satellites are more directly exposed to sunlight and solar wind particles than the surface or the deeper atmospheric layers. At the altitudes where the associated energy is deposited, the atmospheres may become ionized and are referred to as ionospheres. The details of the photon and particle interactions with the upper atmosphere depend strongly on whether the object has anintrinsic magnetic field that may channel the precipitating particles into the atmosphere or drive the atmospheric gas out to space. Important implications of these interactions include atmospheric loss over diverse timescales, photochemistry and the formation of aerosols, which affect the evolution, composition and remote sensing of the planets (satellites). The upper atmosphere connects the planet (satellite) bulk composition to the near-planet (-satellite) environment. Understanding the relevant physics and chemistry provides insight to the past and future conditions of these objects, which is critical for understanding their evolution. This chapter introduces the basic concepts of upper atmospheres and ionospheres in our solar system, and discusses aspects of their neutral and ion composition, wind dynamics and energy budget. This knowledge is key to putting in context the observations of upper atmospheres and haze on exoplanets, and to devise a theory that explains exoplanet demographics.Comment: Invited Revie

Semantic Dementia: a specific network-opathy

Semantic dementia (SD) is a unique syndrome in the frontotemporal lobar degeneration spectrum. Typically presenting as a progressive, fluent anomic aphasia, SD is the paradigmatic disorder of semantic memory with a characteristic anatomical profile of asymmetric, selective antero-inferior temporal lobe atrophy. Histopathologically, most cases show a specific pattern of abnormal deposition of protein TDP-43. This relatively close clinical, anatomical and pathological correspondence suggests SD as a promising target for future therapeutic trials. Here, we discuss outstanding nosological and neurobiological challenges posed by the syndrome and propose a pathophysiological model of SD based on sequential, regionally determined disintegration of a vulnerable neural network

Motion dazzle and camouflage as distinct anti-predator defenses.

BACKGROUND: Camouflage patterns that hinder detection and/or recognition by antagonists are widely studied in both human and animal contexts. Patterns of contrasting stripes that purportedly degrade an observer's ability to judge the speed and direction of moving prey ('motion dazzle') are, however, rarely investigated. This is despite motion dazzle having been fundamental to the appearance of warships in both world wars and often postulated as the selective agent leading to repeated patterns on many animals (such as zebra and many fish, snake, and invertebrate species). Such patterns often appear conspicuous, suggesting that protection while moving by motion dazzle might impair camouflage when stationary. However, the relationship between motion dazzle and camouflage is unclear because disruptive camouflage relies on high-contrast markings. In this study, we used a computer game with human subjects detecting and capturing either moving or stationary targets with different patterns, in order to provide the first empirical exploration of the interaction of these two protective coloration mechanisms. RESULTS: Moving targets with stripes were caught significantly less often and missed more often than targets with camouflage patterns. However, when stationary, targets with camouflage markings were captured less often and caused more false detections than those with striped patterns, which were readily detected. CONCLUSIONS: Our study provides the clearest evidence to date that some patterns inhibit the capture of moving targets, but that camouflage and motion dazzle are not complementary strategies. Therefore, the specific coloration that evolves in animals will depend on how the life history and ontogeny of each species influence the trade-off between the costs and benefits of motion dazzle and camouflage.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A combined model reduction algorithm for controlled biochemical systems

Background: Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often, however, not all portions of a model are of equal interest in a given setting. In such situations methods of model reduction offer one possible approach for addressing the issue of complexity by seeking to eliminate those portions of a pathway that can be shown to have the least effect upon the properties of interest. Methods: In this paper a model reduction algorithm bringing together the complementary aspects of proper lumping and empirical balanced truncation is presented. Additional contributions include the development of a criterion for the selection of state-variable elimination via conservation analysis and use of an ‘averaged’ lumping inverse. This combined algorithm is highly automatable and of particular applicability in the context of ‘controlled’ biochemical networks. Results: The algorithm is demonstrated here via application to two examples; an 11 dimensional model of bacterial chemotaxis in Escherichia coli and a 99 dimensional model of extracellular regulatory kinase activation (ERK) mediated via the epidermal growth factor (EGF) and nerve growth factor (NGF) receptor pathways. In the case of the chemotaxis model the algorithm was able to reduce the model to 2 state-variables producing a maximal relative error between the dynamics of the original and reduced models of only 2.8% whilst yielding a 26 fold speed up in simulation time. For the ERK activation model the algorithm was able to reduce the system to 7 state-variables, incurring a maximal relative error of 4.8%, and producing an approximately 10 fold speed up in the rate of simulation. Indices of controllability and observability are additionally developed and demonstrated throughout the paper. These provide insight into the relative importance of individual reactants in mediating a biochemical system’s input-output response even for highly complex networks. Conclusions: Through application, this paper demonstrates that combined model reduction methods can produce a significant simplification of complex Systems Biology models whilst retaining a high degree of predictive accuracy. In particular, it is shown that by combining the methods of proper lumping and empirical balanced truncation it is often possible to produce more accurate reductions than can be obtained by the use of either method in isolation

Characterization of Multi-Functional Properties and Conformational Analysis of MutS2 from Thermotoga maritima MSB8

The MutS2 homologues have received attention because of their unusual activities that differ from those of MutS. In this work, we report on the functional characteristics and conformational diversities of Thermotoga maritima MutS2 (TmMutS2). Various biochemical features of the protein were demonstrated via diverse techniques such as scanning probe microscopy (SPM), ATPase assays, analytical ultracentrifugation, DNA binding assays, size chromatography, and limited proteolytic analysis. Dimeric TmMutS2 showed the temperature-dependent ATPase activity. The non-specific nicking endonuclease activities of TmMutS2 were inactivated in the presence of nonhydrolytic ATP (ADPnP) and enhanced by the addition of TmMutL. In addition, TmMutS2 suppressed the TmRecA-mediated DNA strand exchange reaction in a TmMutL-dependent manner. We also demonstrated that small-angle X-ray scattering (SAXS) analysis of dimeric TmMutS2 exhibited nucleotide- and DNA-dependent conformational transitions. Particularly, TmMutS2-ADPnP showed the most compressed form rather than apo-TmMutS2 and the TmMutS2-ADP complex, in accordance with the results of biochemical assays. In the case of the DNA-binding complexes, the stretched conformation appeared in the TmMutS2-four-way junction (FWJ)-DNA complex. Convergences of biochemical- and SAXS analysis provided abundant information for TmMutS2 and clarified ambiguous experimental results

RNA activation of haploinsufficient Foxg1 gene in murine neocortex

More than one hundred distinct gene hemizygosities are specifically linked to epilepsy, mental retardation, autism, schizophrenia and neuro-degeneration. Radical repair of these gene deficits via genome engineering is hardly feasible. The same applies to therapeutic stimulation of the spared allele by artificial transactivators. Small activating RNAs (saRNAs) offer an alternative, appealing approach. As a proof-of-principle, here we tested this approach on the Rett syndrome-linked, haploinsufficient, Foxg1 brain patterning gene. We selected a set of artificial small activating RNAs (saRNAs) upregulating it in neocortical precursors and their derivatives. Expression of these effectors achieved a robust biological outcome. saRNA-driven activation (RNAa) was limited to neural cells which normally express Foxg1 and did not hide endogenous gene tuning. saRNAs recognized target chromatin through a ncRNA stemming from it. Gene upregulation required Ago1 and was associated to RNApolII enrichment throughout the Foxg1 locus. Finally, saRNA delivery to murine neonatal brain replicated Foxg1-RNAa in vivo

Locus-specific epigenetic remodeling controls addiction- and depression-related behaviors

Chronic exposure to drugs of abuse or stress regulates transcription factors, chromatin-modifying enzymes and histone post-translational modifications in discrete brain regions. Given the promiscuity of the enzymes involved, it has not yet been possible to obtain direct causal evidence to implicate the regulation of transcription and consequent behavioral plasticity by chromatin remodeling that occurs at a single gene. We investigated the mechanism linking chromatin dynamics to neurobiological phenomena by applying engineered transcription factors to selectively modify chromatin at a specific mouse gene in vivo. We found that histone methylation or acetylation at the Fosb locus in nucleus accumbens, a brain reward region, was sufficient to control drug- and stress-evoked transcriptional and behavioral responses via interactions with the endogenous transcriptional machinery. This approach allowed us to relate the epigenetic landscape at a given gene directly to regulation of its expression and to its subsequent effects on reward behavior

The Impact of Local Genome Sequence on Defining Heterochromatin Domains

Characterizing how genomic sequence interacts with trans-acting regulatory factors to implement a program of gene expression in eukaryotic organisms is critical to understanding genome function. One means by which patterns of gene expression are achieved is through the differential packaging of DNA into distinct types of chromatin. While chromatin state exerts a major influence on gene expression, the extent to which cis-acting DNA sequences contribute to the specification of chromatin state remains incompletely understood. To address this, we have used a fission yeast sequence element (L5), known to be sufficient to nucleate heterochromatin, to establish de novo heterochromatin domains in the Schizosaccharomyces pombe genome. The resulting heterochromatin domains were queried for the presence of H3K9 di-methylation and Swi6p, both hallmarks of heterochromatin, and for levels of gene expression. We describe a major effect of genomic sequences in determining the size and extent of such de novo heterochromatin domains. Heterochromatin spreading is antagonized by the presence of genes, in a manner that can occur independent of strength of transcription. Increasing the dosage of Swi6p results in increased heterochromatin proximal to the L5 element, but does not result in an expansion of the heterochromatin domain, suggesting that in this context genomic effects are dominant over trans effects. Finally, we show that the ratio of Swi6p to H3K9 di-methylation is sequence-dependent and correlates with the extent of gene repression. Taken together, these data demonstrate that the sequence content of a genomic region plays a significant role in shaping its response to encroaching heterochromatin and suggest a role of DNA sequence in specifying chromatin state

Methods of model reduction for large-scale biological systems: a survey of current methods and trends

Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed