1,721,049 research outputs found
Acute aortic dissection and pregnancy: Review and meta‐analysis of incidence, presentation, and pathologic substrates
No full textObjectives: Pregnancy has been recognized as a predisposing factor for acute aortic dissection (AAD) although its occurrence is quite rare. Currently, no trial and few prospective studies exist about this catastrophic event. The present review and meta-analysis aims to update information on clinical presentation, potential risk factors, treatment, and outcome of acute dissection during pregnancy and puerperium. Methods: A comprehensive search of three databases was performed to identify all patients reported in articles published from January 1987. A proportional single-arm meta-analysis with random-effects model was used to pool these variables: risk factors, pregnancy/postpartum occurrence, surgical characteristics, and outcomes. Results: A total of 11 reports and 85 patients with pregnancy-related AAD were available for this study. The prevalence of connective tissue disorders was 62%, Marfan syndrome being the most common. Out of 76 patients, 46 (61%) had dissection during pregnancy and 30 (39%) during puerperium; 40% of events occurred in primigravidae and 60% in multigravidae. Type A and type B dissection occurred in 67% vs 33% of patients. Surgery was performed in 73% of cases with a maternal and fetal mortality of 23% and 27%, respectively. Conclusions: Throughout pregnancy, AAD is quite rare but fatal, especially in Marfan and Loeys–Dietz syndromes, while isolated bicuspid aortic valve is not a risk factor. Even in Marfan syndrome, pathogenesis and evolution of the disease are still unclear. Occurrence of dissection also during puerperium indicates the need for continuous counselling and aortic size monitoring in women at-risk
Replica-symmetry breaking and long-term memory in large games with heterogeneous players
No full text
Relationship between fitness and heterogeneity in exponentially growing microbial populations
Full text linkDespite major environmental and genetic differences, microbial metabolic
networks are known to generate consistent physiological outcomes across vastly
different organisms. This remarkable robustness suggests that, at least in
bacteria, metabolic activity may be guided by universal principles. The
constrained optimization of evolutionarily-motivated objective functions like
the growth rate has emerged as the key theoretical assumption for the study of
bacterial metabolism. While conceptually and practically useful in many
situations, the idea that certain functions are optimized is hard to validate
in data. Moreover, it is not always clear how optimality can be reconciled with
the high degree of single-cell variability observed in experiments within
microbial populations. To shed light on these issues, we develop an inverse
modeling framework that connects the fitness of a population of cells
(represented by the mean single-cell growth rate) to the underlying metabolic
variability through the Maximum-Entropy inference of the distribution of
metabolic phenotypes from data. While no clear objective function emerges, we
find that, as the medium gets richer, the fitness and inferred variability for
Escherichia coli populations follow and slowly approach the theoretically
optimal bound defined by minimal reduction of variability at given fitness.
These results suggest that bacterial metabolism may be crucially shaped by a
population-level trade-off between growth and heterogeneity.Comment: 12+30 pages (includes Supporting Text
Exploration-exploitation tradeoffs dictate the optimal distributions of phenotypes for populations subject to fitness fluctuations
Full text linkWe study a minimal model for the growth of a phenotypically heterogeneous population of cells subject to a fluctuating environment in which they can replicate (by exploiting available resources) and modify their phenotype within a given landscape (thereby exploring novel configurations). The model displays an exploration-exploitation trade-off whose specifics depend on the statistics of the environment. Most notably, the phenotypic distribution corresponding to maximum population fitness (i.e., growth rate) requires a nonzero exploration rate when the magnitude of environmental fluctuations changes randomly over time, while a purely exploitative strategy turns out to be optimal in two-state environments, independently of the statistics of switching times. We obtain analytical insight into the limiting cases of very fast and very slow exploration rates by directly linking population growth to the features of the environment
Metabolic diversity in cell populations: probability densities over the flux polytope
Full text linkEven in clonal populations, cells appear to be strongly heterogeneous in terms of, e.g., protein levels, RNA levels, sizes at birth or division, interdivision times and elongation rates. Part of this variability is likely due to the inherent stochasticity of gene expression at the level of single cells. It is however known that heterogeneous populations may possess an evolutionary advantage, for instance in variable environments or under stress. Despite appearing to be at odds with the idea of optimality presented in the previous chapters, metabolic diversity can be described and modeled within the constraint-based framework introduced in the previous chapters. Specifically, a statistical representation of heterogeneous populations can be obtained by defining suitable probability distributions on the flux polytope. This chapter addresses
• the different sources of variation that affect microbial metabolism along with the mechanisms that may favor higher variability,
• the methods devised to represent heterogeneous microbial populations within the framework of constraint- based models, and
• how these approaches connect to the optimality scenario presented in the previous chapters
ON NON-ERGODIC PHASES IN MINORITY GAMES
No full text In the first part of this review, we survey the known analytic results regarding the non-ergodic phase of standard Minority Games. Such phases are characterized by the fact that, at odds with ergodic regimes, the steady state properties of the game (e.g. the volatility) depend both on the initial conditions chosen for the agents' learning process as well as on the learning rate. Secondly, we present a discussion of the effects of finite-memory learning, which lifts the non-ergodicity, in the context of spherical Minority Games. </jats:p
Effective noisy dynamics within the phenotypic space of a growth-rate maximizing population
No full textMicrobial systems exhibit marked variability in metabolic phenotypes. A recently-proposed class of models explains this feature within a minimal mathematical setup which assumes that populations evolve towards maximum growth rate in a 'phenotypic space' subject to an intrinsic 'diffusive' stochasticity that causes small random changes in single-cell phenotypes. In such a framework, variability results from the exploration-exploitation balance between hardly accessible fast-growing phenotypes and easily accessible slow-growing ones. Here we extend the above scheme to include a degree of extrinsic noise, showing that the population dynamics over the phenotypic space is captured by an effective process that conflates both sources of randomness. This in turn leads to a simple approximation for the asymptotic distribution of the population over the phenotypic space, highlighting the connection between the strength of the noise that affects the dynamics and the degree of optimization. The theory thus obtained displays an excellent agreement with numerical simulations of low-dimensional systems
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