Life Cycle-Dependent Cytoskeletal Modifications in Plasmodium falciparum Infected Erythrocytes

10.1371/journal.pone.0061170PLoS ONE84

Observations on comatose survivors of cardiopulmonary resuscitation with generalized myoclonus

BACKGROUND: There is only limited data on improvements of critical medical care is resulting in a better outcome of comatose survivors of cardiopulmonary resuscitation (CPR) with generalized myoclonus. There is also a paucity of data on the temporal dynamics of electroenephalographic (EEG) abnormalities in these patients. METHODS: Serial EEG examinations were done in 50 comatose survivors of CPR with generalized myoclonus seen over an 8 years period. RESULTS: Generalized myoclonus occurred within 24 hours after CPR. It was associated with burst-suppression EEG (n = 42), continuous generalized epileptiform discharges (n = 5), alpha-coma-EEG (n = 52), and low amplitude (10 μV <) recording (n = 1). Except in 3 patients, these EEG-patterns were followed by another of these always nonreactive patterns within one day, mainly alpha-coma-EEG (n = 10) and continuous generalized epileptiform discharges (n = 9). Serial recordings disclosed a variety of EEG-sequences composed of these EEG-patterns, finally leading to isoelectric or flat recordings. Forty-five patients died within 2 weeks, 5 patients survived and remained in a permanent vegetative state. CONCLUSION: Generalized myoclonus in comatose survivors of CPR still implies a poor outcome despite advances in critical care medicine. Anticonvulsive drugs are usually ineffective. All postanoxic EEG-patterns are transient and followed by a variety of EEG sequences composed of different EEG patterns, each of which is recognized as an unfavourable sign. Different EEG-patterns in anoxic encephalopathy may reflect different forms of neocortical dysfunction, which occur at different stages of a dynamic process finally leading to severe neuronal loss

Co-evolutionary dynamics of collective action with signaling for a quorum

Collective signaling for a quorum is found in a wide range of organisms that face collective action problems whose successful solution requires the participation of some quorum of the individuals present. These range from humans, to social insects, to bacteria. The mechanisms involved, the quorum required, and the size of the group may vary. Here we address the general question of the evolution of collective signaling at a high level of abstraction. We investigate the evolutionary dynamics of a population engaging in a signaling N-person game theoretic model. Parameter settings allow for loners and cheaters, and for costly or costless signals. We find a rich dynamics, showing how natural selection, operating on a population of individuals endowed with the simplest strategies, is able to evolve a costly signaling system that allows individuals to respond appropriately to different states of Nature. Signaling robustly promotes cooperative collective action, in particular when coordinated action is most needed and difficult to achieve. Two different signaling systems may emerge depending on Nature's most prevalent states.Funding: This research was supported by FEDER through POFC - COMPETE, FCT-Portugal through grants SFRH/BD/86465/2012, PTDC/MAT/122897/2010, EXPL/EEI-SII/2556/2013, and by multi-annual funding of CMAF-UL, CBMA-UM and INESC-ID (under the projects PEst-OE/BIA/UI4050/2014 and UID/CEC/50021/2013) provided by FCT-Portugal, and by Fundacao Calouste Gulbenkian through the "Stimulus to Research" program for young researchers. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Biochemical systems identification by a random drift particle swarm optimization approach

BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

Stable carbon and nitrogen isotope enrichment in primate tissues

Isotopic studies of wild primates have used a wide range of tissues to infer diet and model the foraging ecologies of extinct species. The use of mismatched tissues for such comparisons can be problematic because differences in amino acid compositions can lead to small isotopic differences between tissues. Additionally, physiological and dietary differences among primate species could lead to variable offsets between apatite carbonate and collagen. To improve our understanding of the isotopic chemistry of primates, we explored the apparent enrichment (ε*) between bone collagen and muscle, collagen and fur or hair keratin, muscle and keratin, and collagen and bone carbonate across the primate order. We found that the mean ε* values of proteinaceous tissues were small (≤1‰), and uncorrelated with body size or phylogenetic relatedness. Additionally, ε* values did not vary by habitat, sex, age, or manner of death. The mean ε* value between bone carbonate and collagen (5.6 ± 1.2‰) was consistent with values reported for omnivorous mammals consuming monoisotopic diets. These primate-specific apparent enrichment values will be a valuable tool for cross-species comparisons. Additionally, they will facilitate dietary comparisons between living and fossil primates

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

Nonlinear and Multiplayer Evolutionary Games

Classical evolutionary game theory has typically considered populations within which randomly selected pairs of individuals play games against each other, and the resulting payoff functions are linear. These simple functions have led to a number of pleasing results for the dynamic theory, the static theory of evolutionarily stable strategies, and the relationship between them. We discuss such games, together with a basic introduction to evolutionary game theory, in Sect. 5.1. Realistic populations, however, will generally not have these nice properties, and the payoffs will be nonlinear. In Sect. 5.2 we discuss various ways in which nonlinearity can appear in evolutionary games, including pairwise games with strategy-dependent interaction rates, and playing the field games, where payoffs depend upon the entire population composition, and not on individual games. In Sect. 5.3 we consider multiplayer games, where payoffs are the result of interactions between groups of size greater than two, which again leads to nonlinearity, and a breakdown of some of the classical results of Sect. 5.1. Finally in Sect. 5.4 we summarise and discuss the previous sections