Prefrontal afferents to the dorsal raphe nucleus in the rat

The prefrontal cortex (PFC) receives strong inputs from monoaminergic cell groups in the brainstem and also sends projections to these nuclei. Recent evidence suggests that the PFC exerts a powerful top-down control over the dorsal raphe nucleus (DR) and that it may be involved in the actions of pharmaceutical drugs and drugs of abuse. In the light of these findings, the precise origin of prefrontal inputs to DR was presently investigated by using the cholera toxin subunit b (CTb) as retrograde tracer. All the injections placed in DR produced retrograde labeling in the medial, orbital, and lateral divisions of the PFC as well as in the medial part of the frontal polar cortex. The labeling was primarily located in layer V. Remarkably, labeling in the medial PFC was denser in its ventral part (infralimbic and ventral prelimbic cortices) than in its dorsal part (dorsal prelimbic, anterior cingulate and medial precentral cortices). After injections in the rostral or caudal DR, the largest number of labeled neurons was observed in the medial PFC, whereas after injections in the mid-rostrocaudal DR, the labeled neurons were more homogeneously distributed in the three main PFC divisions. A cluster of labeled neurons also was observed around the apex of the rostral pole of the accumbens, especially after rostral and mid-rostrocaudal DR injections. Overall, these results confirm the existence of robust preftontal projections to DR, mainly derived from the ventral part of the medial PFC, and underscore a substantial contribution of the frontal polar cortex. (C) 2008 Elsevier Inc. All rights reserved.

Effects of intensity and positional predictability of a visual stimulus on simple reaction time

The influence of visual stimuli intensity on manual reaction time (RT) was investigated under two different attentional settings: high (Experiment 1) and low (Experiment 2) stimulus location predictability. These two experiments were also run under both binocular and monocular viewing conditions. We observed that RT decreased as stimulus intensity increased. It also decreased as the viewing condition was changed from monocular to binocular as well as the location predictability shifted from low to high. A significant interaction was found between stimulus intensity and viewing condition, but no interaction was observed between neither of these factors and location predictability. These findings support the idea that the stimulus intensity effect arises from purely sensory, pre-attentive mechanisms rather than deriving from more efficient attentional capture. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Upper limit on the cosmic-ray photon fraction at EeV energies from the Pierre Auger Observatory

From direct observations of the longitudinal development of ultra-high energy air showers performed with the Pierre Auger Observatory, upper limits of 3.8%, 2.4%, 3.5% and 11.7% (at 95% c.l.) are obtained on the fraction of cosmic-ray photons above 2, 3, 5 and 10 EeV (1 EeV equivalent to 10(18) eV), respectively. These are the first experimental limits on ultra-high energy photons at energies below 10 EeV. The results complement previous constraints on top-down models from array data and they reduce systematic uncertainties in the interpretation of shower data in terms of primary flux, nuclear composition and proton-air cross-section. (C) 2009 Elsevier B.V. All rights reserved.