The left ventricle increases contractility in response to baroreceptor unloading, which is sympathetically mediated in the anesthetized rat.

Contemporary discussion of the baroreflex includes the efferent vascular-sympathetic and cardiovagal arms. Since sympathetic postganglionic neurons also innervate the left ventricle (LV), it is often assumed that the LV produces a sympathetically mediated increase in contractility during baroreceptor unloading, but this has not been characterized using a load-independent index of contractility. We aimed to determine 1) whether LV contractility increases in response to baroreceptor unloading and 2) whether such increases are mediated via the sympathetic or parasympathetic arm of the autonomic nervous system. Ten male Wistar rats were anesthetized (urethane) and instrumented with arterial and LV pressure-volume catheters to measure mean arterial pressure (MAP) and load-independent LV contractility [maximal rate of increase in pressure adjusted to end-diastolic volume (PAdP/dtmax)], respectively. Rats were placed in a servo-controlled lower-body negative pressure (LBNP) chamber to reduce MAP by 10% for 60 s to mechanically unload baroreceptors under control conditions. LBNP was repeated in each animal following infusions of cardiac autonomic blockers using esmolol (sympathetic), atropine (parasympathetic), and esmolol + atropine. Under control conditions, PAdP/dtmax increased during baroreceptor unloading (26 ± 6 vs. 31 ± 9 mmHg·s-1·µL-1, P = 0.031). During esmolol, there was no increase in LV contractility during baroreceptor unloading (11 ± 2 vs. 12 ± 2, P = 0.125); however, during atropine, there was an increase in LV contractility during baroreceptor unloading (26 ± 6 vs. 31 ± 9, P = 0.019). During combined esmolol and atropine, there was a small increase in contractility versus control (13 ± 3 vs. 15 ± 4, P = 0.046). Our results demonstrate that, in anesthetized rats, LV contractility increases in response to baroreceptor unloading, which is largely sympathetically mediated.NEW & NOTEWORTHY This study empirically demonstrates a sympathetically mediated increase in LV contractility in response to baroreceptor unloading using a load-independent index of cardiac contractility in the anesthetized rat.

To help or punish in the face of unfairness: men and women prefer mutually-beneficial strategies over punishment in a sexual selection context.

Consistent with a sexual selection account of cooperation, based on female choice, men, in romantic contexts, in general display mutually-beneficial behaviour and women choose men who do so. This evidence is based on a two-choice-architecture (cooperate or not). Here we extend this to include punishment options using a four-choice-architecture ('punishing a transgressor', 'compensating a victim', 'both punishing and compensating' or 'doing nothing'). Both compensation (a self-serving mutually-beneficial behaviour) and self-serving punishment, are associated with positive mate qualities. We test which is preferred by males and chosen by female undergraduates. We further explore effects of trait empathy and political ideology on these preferences. In a series of three studies using a third-party punishment and compensation (3PPC) game we show (Study One), that romantically-primed undergraduate males, express a preference to either 'compensate' or 'both compensate and punish', and undergraduate women find males who 'compensate' or 'compensate and punish' the most attractive (Studies Two and Three). Compensating men are perceived as compassionate, fair and strong by undergraduate women (Study Three). High trait empathy (Studies One and Three) and a left-wing political ideology (Study Three) are associated with a preference for compensation. Thus, self-serving mutually-beneficial behaviour can be preferred over self-serving punishment as a signal of mate quality in undergraduates. Implications for the evolution of cooperation are discussed with respect to sexual selection.

Learning flexible features for conditional random fields.

Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features. These features can be functions of arbitrary combinations of observations, labels and auxiliary hidden variables. We also present a simple induction scheme to learn these features, which can automatically determine the complexity needed for a given data set. We apply the model to two real-world tasks, information extraction and image labeling, and compare our results to several other methods for discriminative labeling.