Defeat is a social stressor involving subordination by a threatening conspecific. Type 2 corticotropin-releasing factor receptors (CRF2) are abundant in brain regions implicated in defeat responses and are putative stress-related molecules. The present study sought to determine whether neuroactivation and CRF2 expression co-occurred at brain region or cellular levels following acute defeat. Male “intruder” Wistar rats were placed into the cage of an aggressive “resident” Long-Evans rat (n=6). Upon defeat, intruders (n=6) were placed in a wire-mesh chamber and were returned to the resident’s cage for an additional 75 min. Controls (n=6) were handled and returned to their home cage for the same duration. Coronal brain sections were stained for an immediate early gene product, Fos, as a neuronal activation marker. Combined immunohistochemistry with in situ hybridization was performed on a subset of brain sections from defeated intruders to visualize Fos immunoreactivity and CRF2 mRNA jointly. Defeated rats had fivefold, sevenfold, and 10-fold more Fos-positive cells than controls in the arcuate, ventromedial nucleus of the hypothalamus, and medial amygdala post-defeat. Significant colocalization of CRF2 mRNA and Fos-positive cells was observed in the posterior medial amygdala but not in the arcuate nucleus or ventromedial hypothalamus. The results indicate CRF2 receptor-positive neurons in the posterior medial amygdala are involved in the neural response to social defeat.

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Quantitative molecular similarity analysis (QMSA) is a seemingly useful tool for estimating environmental properties for the hundreds of emerging contaminants that have not yet been fully evaluated. Moreover, calibrated QMSA models are also useful for prioritizing research among currently unmeasured chemicals of interest. Previous work has demonstrated that prioritization based on molecular 'representativeness', as parameterized using summed Euclidean distances in n dimensions corresponding to n molecular descriptors, improves the prediction accuracy of QMSA models compared to random selection of compounds to be measured. In this study, we use two datasets of environmental parameters (i.e. in vitro oestrogenicity and sorption distribution coefficient Kd ) to demonstrate that maximizing representativeness alone cannot deliver optimal improvement in prediction accuracy if many of the chemicals that have already been measured are themselves highly representative. Thus, proper QMSA-based prioritization among unmeasured chemicals constitutes a balance between maximizing representativeness and minimizing redundancy. It is demonstrated that redundancy considerations are especially critical for highly heterogeneous datasets, and some discussion about achieving a proper balance between the two prioritization criteria is presented.