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Search Results: 1 - 10 of 224166 matches for " Dante R; Bolarte "
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Outbreak of persistent cutaneous abscesses due to Mycobacterium chelonae after mesotherapy sessions, Lima, Peru
Munayco,César V; Grijalva,Carlos G; Culqui,Dante R; Bolarte,José L; Suárez-Ognio,Luis A; Quispe,Neyda; Calderon,Roger; Ascencios,Luis; Del Solar,Manuel; Salomón,Martín; Bravo,Francisco; Gotuzzo,Eduardo;
Revista de Saúde Pública , 2008, DOI: 10.1590/S0034-89102008000100020
Abstract: outbreaks of rapidly growing mycobacteria have been occasionally described. the article reports an outbreak of cutaneous abscesses due to mycobacterium chelonae following mesotherapy in lima, peru. from december 2004 through january 2005, 35 subjects who had participated in mesotherapy training sessions presented with persistent cutaneous abscesses. thirteen (37%) of these suspected cases consented to underwent clinical examination. skin punch-biopsies were collected from suspicious lesions and substances injected during mesotherapy were analyzed. suspected cases were mainly young women and lesions included subcutaneous nodules, abscesses and ulcers. mycobacterium chelonae was isolated from four patients and from a procaine vial. in conclusion, it is important to consider mesotherapy as a potential source of rapidly growing mycobacteria infections.
Critical brain networks
Dante R. Chialvo
Physics , 2004, DOI: 10.1016/j.physa.2004.05.064
Abstract: Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevents information flow. We discuss the idea put forward by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations.
Iterated Brownian motion in an open set
R. Dante DeBlassie
Mathematics , 2004, DOI: 10.1214/105051604000000404
Abstract: Suppose a solid has a crack filled with a gas. If the crack reaches the surrounding medium, how long does it take the gas to diffuse out of the crack? Iterated Brownian motion serves as a model for diffusion in a crack. If \tau is the first exit time of iterated Brownian motion from the solid, then P(\tau>t) can be viewed as a measurement of the amount of contaminant left in the crack at time t. We determine the large time asymptotics of P(\tau>t) for both bounded and unbounded sets. We also discuss a strange connection between iterated Brownian motion and the parabolic operator {1/8}\Delta^2-\frac{\partial}{\partial t}.
The brain near the edge
Dante R. Chialvo
Quantitative Biology , 2006, DOI: 10.1063/1.2709580
Abstract: When viewed at a certain coarse grain, the brain seems a relatively small dynamical system composed by a few dozen interacting areas, performing a number of stereotypical behaviors. It is known that, even relatively small dynamical systems can reliably generate robust and flexible behavior if they are possed near a second order phase transition, because of the abundance of metastable states at the critical point. The approach pursued here assumes that some of the most fundamental properties of the functioning brain are possible because it is spontaneously possed at the border of such instability. In this notes we review the motivation, the arguments and recent results as well as the implications of this view of the functioning brain.
Emergent complex neural dynamics
Dante R. Chialvo
Quantitative Biology , 2010, DOI: 10.1038/nphys1803
Abstract: A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain.
Emergent complexity: what uphill analysis or downhill invention can not do
Dante R. Chialvo
Quantitative Biology , 2008,
Abstract: In these notes we review emergent phenomena in complex systems, emphasizing ways to identify potential underlying universal mechanisms that generates complexity. The discussion is centered around the emergence of collective behavior in dynamical systems when they are poised near a critical point of a phase transition, either by tuning or by self-organization. We then argue the rationale for our proposal that the brain is naturally poised near criticality reviewing recent results as well as the implications of this view of the functioning brain.
Critical brain dynamics at large scale
Dante R. Chialvo
Quantitative Biology , 2012,
Abstract: Highly correlated brain dynamics produces synchronized states with no behavioral value, while weakly correlated dynamics prevent information flow. In between these states, the unique dynamical features of the critical state endow the brain with properties which are fundamental for adaptive behavior. We discuss the idea put forward two decades ago by Per Bak that the working brain stays at an intermediate (critical) regime characterized by power-law correlations. This proposal is now supported by a wide body of empirical evidence at different scales demonstrating that the spatiotemporal brain dynamics exhibit key signatures of critical dynamics, previously recognized in other complex systems. The rationale behind this program is discussed in these notes, followed by an account of the most recent results.
What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations
Daniel Fraiman,Dante R. Chialvo
Frontiers in Physiology , 2012, DOI: 10.3389/fphys.2012.00307
Abstract: The study of spontaneous fluctuations of brain activity, often referred as brain noise, is getting increasing attention in functional magnetic resonance imaging (fMRI) studies. Despite important efforts, much of the statistical properties of such fluctuations remain largely unknown. This work scrutinizes these fluctuations looking at specific statistical properties which are relevant to clarify its dynamical origins. Here, three statistical features which clearly differentiate brain data from naive expectations for random processes are uncovered: First, the variance of the fMRI mean signal as a function of the number of averaged voxels remains constant across a wide range of observed clusters sizes. Second, the anomalous behavior of the variance is originated by bursts of synchronized activity across regions, regardless of their widely different sizes. Finally, the correlation length (i.e., the length at which the correlation strength between two regions vanishes) as well as mutual information diverges with the cluster's size considered, such that arbitrarily large clusters exhibit the same collective dynamics than smaller ones. These three properties are known to be exclusive of complex systems exhibiting critical dynamics, where the spatio-temporal dynamics show these peculiar type of fluctuations. Thus, these findings are fully consistent with previous reports of brain critical dynamics, and are relevant for the interpretation of the role of fluctuations and variability in brain function in health and disease.
Learning from Mistakes
Dante R. Chialvo,Per Bak
Physics , 1997,
Abstract: A simple model of self-organised learning with no classical (Hebbian) reinforcement is presented. Synaptic connections involved in mistakes are depressed. The model operates at a highly adaptive, probably critical, state reached by extremal dynamics similar to that of recent evolution models. Thus, one might think of the mechanism as synaptic Darwinism.
Adaptive learning by extremal dynamics and negative feedback
Per Bak,Dante R Chialvo
Physics , 2000, DOI: 10.1103/PhysRevE.63.031912
Abstract: We describe a mechanism for biological learning and adaptation based on two simple principles: (I) Neuronal activity propagates only through the network's strongest synaptic connections (extremal dynamics), and (II) The strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift adaptation to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated nonlinear tasks, even in the presence of noise. In particular, the learning time for the benchmark parity problem scales algebraically with the problem size N, with an exponent $k\sim 1.4$.
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