No abstract

Convergent evidence indicates that in later stages of Parkinson's disease raphestriatal serotonin neurons compensate for the loss of nigrostriatal dopamine neurons by converting and releasing dopamine derived from exogenous administration of the pharmacotherapeutic L-3,4-dihydroxyphenyl-L-alanine (L-DOPA). Because the serotonin system is not equipped with dopamine autoregulatory mechanisms, it has been postulated that raphe-mediated striatal dopamine release may fluctuate dramatically. These fluctuations may portend the development of abnormal involuntary movements called L-DOPA-induced dyskinesia (LID). As such, it has been hypothesized that reducing the activity of raphestriatal neurons could dampen supraphysiological stimulation of striatal dopamine receptors thereby alleviating LID. To directly address this, the current study employed the rodent model of LID to investigate the contribution of the rostral raphe nuclei (RRN) in the development, expression and treatment of LID. In the first study, dual serotonin/dopamine selective lesions of the RRN and medial forebrain bundle, respectively, verified that the RRN are essential for the development of LID. In a direct investigation into the neuroanatomical specificity of these effects, microinfusions of ±8-OH-DPAT into the intact dorsal raphe nucleus dose-dependently attenuated the expression of LID without affecting the anti-parkinsonian efficacy of L-DOPA. These current findings reveal the integral contribution of the RRN in the development and expression of LID and implicate a prominent role for dorsal raphe 5-HT1AR in the efficacious properties of 5-HT1AR agonists.

Tropical forests exhibit complex but poorly understood patterns of leaf phenology. Understanding species- and individual-level phenological patterns in tropical forests requires datasets covering large numbers of trees, which can be provided by Unmanned Aerial Vehicles (UAVs). In this paper, we test a workflow combining high-resolution RGB images (7 cm/pixel) acquired from UAVs with a machine learning algorithm to monitor tree and species leaf phenology in a tropical forest in Panama. We acquired images for 34 flight dates over a 12-month period. Crown boundaries were digitized in images and linked with forest inventory data to identify species. We evaluated predictions of leaf cover from different models that included up to 14 image features extracted for each crown on each date. The models were trained and tested with visual estimates of leaf cover from 2422 images from 85 crowns belonging to eight species spanning a range of phenological patterns. The best-performing model included both standard color metrics, as well as texture metrics that quantify within-crown variation, with r2 of 0.84 and mean absolute error (MAE) of 7.8% in 10-fold cross-validation. In contrast, the model based only on the widely-used Green Chromatic Coordinate (GCC) index performed relatively poorly (r2 = 0.52, MAE = 13.6%). These results highlight the utility of texture features for image analysis of tropical forest canopies, where illumination changes may diminish the utility of color indices, such as GCC. The algorithm successfully predicted both individual-tree and species patterns, with mean r2 of 0.82 and 0.89 and mean MAE of 8.1% and 6.0% for individual- and species-level analyses, respectively. Our study is the first to develop and test methods for landscape-scale UAV monitoring of individual trees and species in diverse tropical forests. Our analyses revealed undescribed patterns of high intraspecific variation and complex leaf cover changes for some species.

We present enhancements to the Compass algorithm that automatically deduce interchemotype relationships and generate predictive quantitative models of receptor binding based solely on structure-activity data. We applied the technique to a series of compounds assayed for 5-HT1A binding. A model was constructed from 20 compounds of two chemotypes and used to predict the affinities and bioactive conformation of 35 new compounds, most of which had new underlying scaffolds and/or functional groups. The model's mean error of prediction was 0.5 log units (essentially the assay resolution), even on quite divergent series. The predictions are supported by an interpretable hypothesis for the binding determinants of the receptor and the geometric relationships of the chemotypes.

Chronic L-3,4-dihydroxyphenylalanine (L-DOPA) pharmacotherapy in Parkinson's disease is often accompanied by the development of abnormal and excessive movements known as dyskinesia. Clinical and experimental studies indicate that indirect serotonin agonists can suppress dyskinesia without affecting the efficacy of L-DOPA. While the mechanism by which these effects occur is not clear, recent research suggests that serotonin 5-HT1A receptors may play a pivotal role. To test this, male Sprague-Dawley rats with unilateral 6-hydroxydopamine medial forebrain bundle lesions received 1 week of daily treatment with L-DOPA (12 mg/kg, i.p.) plus benserazide (15 mg/kg, i.p.). Beginning on the 8th day of treatment and every 3rd or 4th day thereafter, rats were pretreated with vehicle (0.9% NaCl), the serotonin and dopamine releaser 3,4-methylenedioxymethamphetamine (MDMA; 0.25 or 2.5 mg/kg, i.p.) or the serotonin releaser fenfluramine (FEN; 0.25 or 2.5 mg/kg, i.p.) 5 min prior to L-DOPA, after which abnormal involuntary movements (AIMs) and rotations were quantified every 20th minute for 2 h. Pretreatment with 2.5 mg/kg of either MDMA or FEN reduced AIMs. To determine the contribution of the 5-HT1A receptor to these effects, another group of L-DOPA-primed 6-hydroxydopamine-lesioned rats were pretreated with the 5-HT1A antagonist WAY100635 (0.5 mg/kg, i.p.), MDMA + WAY100635 (2.5 + 0.5 mg/kg, i.p.) or FEN + WAY100635 (2.5 + 0.5 mg/kg, i.p.) 5 min prior to L-DOPA and subsequent AIMs and rotation tests. The antidyskinetic effects of MDMA and FEN were reversed by cotreatment with WAY100635. These results suggest that 5-HT-augmenting compounds such as MDMA and FEN probably convey antidyskinetic properties in part via stimulation of 5-HT1A receptors.