Global convergence in systems of differential equations arising from chemical reaction networks

It is shown that certain classes of differential equations arising from the modelling of chemical reaction networks have the following property: the state space is foliated by invariant subspaces each of which contains a unique equilibrium which, in turn, attracts all initial conditions on the associated subspace

The smallest bimolecular mass-action system with a vertical Andronov–Hopf bifurcation

We present a three-dimensional differential equation, which robustly displays a degenerate Andronov–Hopf bifurcation of infinite codimension, leading to a center, i.e., an invariant two-dimensional surface that is filled with periodic orbits surrounding an equilibrium. The system arises from a three-species bimolecular chemical reaction network consisting of four reactions. In fact, it is the only such mass-action system that admits a center via an Andronov–Hopf bifurcation

P matrix properties, injectivity, and stability in chemical reaction systems

In this paper we examine matrices which arise naturally as Jacobians in chemical dynamics. We are particularly interested in when these Jacobians are P matrices (up to a sign change), ensuring certain bounds on their eigenvalues, precluding certain behaviour such as multiple equilibria, and sometimes implying stability. We first explore reaction systems and derive results which provide a deep connection between system structure and the P matrix property. We then examine a class of systems consisting of reactions coupled to an external rate-dependent negative feedback process, and characterise conditions which ensure the P matrix property survives the negative feedback. The techniques presented are applied to examples published in the mathematical and biological literature

Separable Neural Components in the Processing of Black and White Faces

In a study of the neural components of automatic and controlled social evaluation, White participants viewed Black and White faces during event-related functional magnetic resonance imaging. When the faces were presented for 30 ms, activation in the amygdala—a brain region associated with emotion—was greater for Black than for White faces. When the faces were presented for 525 ms, this difference was significantly reduced, and regions of frontal cortex associated with control and regulation showed greater activation for Black than White faces. Furthermore, greater race bias on an indirect behavioral measure was correlated with greater difference in amygdala activation between Black and White faces, and frontal activity predicted a reduction in Black-White differences in amygdala activity from the 30-ms to the 525-ms condition. These results provide evidence for neural distinctions between automatic and more controlled processing of social groups, and suggest that controlled processes may modulate automatic evaluation.Psycholog

Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation

We used fMRI to explore the neural substrates involved in the unconscious evaluation of Black and White social groups. Specifically, we focused on the amygdala, a subcortical structure known to play a role in emotional learning and evaluation. In Experiment 1, White American subjects observed faces of unfamiliar Black and White males. The strength of amygdala activation to Black-versus-White faces was correlated with two indirect (unconscious) measures of race evaluation (Implicit Association Test [IAT] and potentiated startle), but not with the direct (conscious) expression of race attitudes. In Experiment 2, these patterns were not obtained when the stimulus faces belonged to familiar and positively regarded Black and White individuals. Together, these results suggest that amygdala and behavioral responses to Black-versus-White faces in White subjects reflect cultural evaluations of social groups modified by individual experience.Psycholog

Simulation of Preterm Neonatal Brain Metabolism During Functional Neuronal Activation Using a Computational Model

We present a computational model of metabolism in the preterm neonatal brain. The model has the capacity to mimic haemodynamic and metabolic changes during functional activation and simulate functional near-infrared spectroscopy (fNIRS) data. As an initial test of the model's efficacy, we simulate data obtained from published studies investigating functional activity in preterm neonates. In addition we simulated recently collected data from preterm neonates during visual activation. The model is well able to predict the haemodynamic and metabolic changes from these observations. In particular, we found that changes in cerebral blood flow and blood pressure may account for the observed variability of the magnitude and sign of stimulus-evoked haemodynamic changes reported in preterm infants

False claims about false memory research

Pezdek and Lam [Pezdek, K. & Lam, S. (2007). What research paradigms have cognitive psychologists used to study “False memory,” and what are the implications of these choices? Consciousness and Cognition] claim that the majority of research into false memories has been misguided. Specifically, they charge that false memory scientists have been (1) misusing the term “false memory,” (2) relying on the wrong methodologies to study false memories, and (3) misapplying false memory research to real world situations. We review each of these claims and highlight the problems with them. We conclude that several types of false memory research have advanced our knowledge of autobiographical and recovered memories, and that future research will continue to make significant contributions to how we understand memory and memory errors