We investigated the pharmacology of three novel compounds, Org 27569 (5-chloro-3-ethyl-1H-indole-2-carboxylic acid [2-(4-piperidin-1-yl-phenyl)-ethyl]-amide), Org 27759 (3-ethyl-5-fluoro-1H-indole-2-carboxylic acid [2-94-dimethylamino-phenyl)-ethyl]-amide), and Org 29647 (5-chloro-3-ethyl-1H-indole-2-carboxylic acid (1-benzyl-pyrrolidin-3-yl)-amide, 2-enedioic acid salt), at the cannabinoid CB 1 receptor. In equilibrium binding assays, the Org compounds significantly increased the binding of the CB 1 receptor agonist, indicative of a positively cooperative allosteric effect. The same compounds caused a significant, but incomplete, decrease in the specific binding of the CB 1 receptor inverse agonist studies also validated the allosteric nature of the Org compounds, because they all significantly decreased radioligand dissociation. These data suggest that the Org compounds bind allosterically to the CB 1 receptor and elicit a conformational change that increases agonist affinity for the orthosteric binding site. In contrast to the binding assays, however, the Org compounds behaved as insurmountable antagonists of receptor function; in the reporter gene assay, the guanosine 5Ј-O-(3-[35 S]thio)triphosphate binding assay and the mouse vas deferens assay they elicited a significant reduction in the E max value for CB 1 receptor agonists. The data presented clearly demonstrate, for the first time, that the cannabinoid CB 1 receptor contains an allosteric binding site that can be recognized by synthetic small molecule ligands.Mammalian tissues express at least two types of cannabinoid receptor, CB 1 and CB 2 , both G protein-coupled (for review, see Howlett et al., 2002). CB 1 receptors are found predominantly at central and peripheral nerve terminals where they mediate inhibition of transmitter release. Endogenous ligands for these receptors also exist. These "endocannabinoids" are all eicosanoids, prominent examples including arachidonoylethanolamide (anandamide) and 2-arachidonoyl glycerol, both of which are synthesized on demand, removed from their sites of action by tissue uptake processes and metabolized by intracellular enzymes (Pertwee and Ross,

Since 2006, six satellites measuring solar‐induced chlorophyll fluorescence (SIF) have been launched to better constrain terrestrial gross primary productivity (GPP). The promise of the SIF signal as a proxy for photosynthesis with a strong relationship to GPP has been widely cited in carbon cycling studies. However, chlorophyll fluorescence originates from dynamic energy partitioning at the leaf level and does not exhibit a uniformly linear relationship with photosynthesis at finer scales. We induced stomatal closure in deciduous woody tree branches and measured SIF at a proximal scale, alongside leaf‐level gas exchange, pulse amplitude modulated (PAM) fluorescence, and leaf pigment content. We found no change in SIF or steady‐state PAM fluorescence, despite clear reductions in stomatal conductance, carbon assimilation, and light‐use efficiency in treated leaves. These findings suggest that equating SIF and photosynthesis is an oversimplification that may undermine the utility of SIF as a biophysical parameter in GPP models.

Forest fragmentation is pervasive throughout the world's forests, impacting growing conditions and carbon (C) dynamics through edge effects that produce gradients in microclimate, biogeochemistry, and stand structure. Despite the majority of global forests being <1 km from an edge, our understanding of forest C dynamics is largely derived from intact forest systems. Edge effects on the C cycle vary by biome in their direction and magnitude, but current forest C accounting methods and ecosystem models generally fail to include edge effects. In the mesic northeastern US, large increases in C stocks and productivity are found near the temperate forest edge, with over 23% of the forest area within 30 m of an edge. Changes in the wind, fire, and moisture regimes near tropical forest edges result in decreases in C stocks and productivity. This review explores differences in C dynamics observed across biomes through a trade‐offs framework that considers edge microenvironmental changes and limiting factors to productivity.

Background: At least six synaptic vesicle (SV) membrane proteins must be endocytosed and sorted during SV recycling. Results: Loss of vGlut1 slows endocytosis kinetics of many other SV proteins, whereas impairing vGlut1 slows other cargos. Conclusion: vGlut1 plays a central role in coordinating SV cargo endocytosis. Significance: SV recycling is essential for synaptic transmission and relies on collective behavior of many cargo proteins.

The use of light detection and ranging (LiDAR) techniques for recording and analyzing tree and forest structural variables shows strong promise for improving established hyperspectral-based tree species classifications; however, previous multi-sensoral projects were often limited by error resulting from seasonal or flight path differences. The National Aeronautics and Space Administration (NASA) Goddard's LiDAR, hyperspectral, and thermal imager (G-LiHT) is now providing co-registered data on experimental forests in the United States, which are associated with established ground truths from existing forest plots. Free, user-friendly machine learning applications like the Orange Data Mining Extension for Python recently simplified the process of combining datasets, handling variable redundancy and noise, and reducing dimensionality in remotely sensed datasets. Neural networks, CN2 rules, and support vector machine methods are used here to achieve a final classification accuracy of 67% for dominant tree species in experimental plots of Howland Experimental Forest, a mixed coniferous-deciduous forest with ten dominant tree species, and 59% for plots in Penobscot Experimental Forest, a mixed coniferous-deciduous forest with 15 dominant tree species. These accuracies are higher than those produced using LiDAR or hyperspectral datasets separately, suggesting that combined spectral and structural data have a greater richness of complementary information than either dataset alone. Using greatly simplified datasets created by our dimensionality reduction methodology, machine learner performance remains comparable or higher to that using the full dataset. Across forests, the identification of shared structural and spectral variables suggests that this methodology can successfully identify parameters with high explanatory power for differentiating among tree species, and opens the possibility of addressing large-scale forestry questions using optimized remote sensing workflows.