Distribution of Gaba in the nerve ganglia of haliotis asinina linnaeus

Gamma-aminobutyric acid (GABA) is a major neurotransmitter and effective settlement inducer in abalone aquaculture. This study aimed to explore the distribution of GABA within neural tissues of Haliotis asinina. Gamma-aminobutyric acid was found in neuronal cell type 1 of 3 major ganglia (i.e., cerebral, pleuropedal, and visceral ganglia) of both sexes. The distribution of GABA-immunoreactive (-ir) cells in the cerebral ganglion was concentrated mostly in the cortex region of the dorsal horn, whereas it was scattered throughout the pleuropedal ganglion, with more in the upper half. Gamma-aminobutyric acid-ir nerve fibers were found throughout the neuropils of the ganglia. The visceral ganglion had the least numbers of GABA-ir neurons compared with the other 2 ganglia. The cells were distributed mainly in the dorsal horn. We also observed GABA to be colocalized with 2 other neurotransmitters: serotonin (5-HT) and dopamine (DA). In the cerebral ganglion, fluorescence double staining of GABA and 5-HT, and GABA and DA showed immunoreactivity in separate cells and was also colocalized in the same cells. In the pleuropedal ganglion, the staining pattern was similar to the cerebral ganglion, but positive-staining cells were less numerous. In the visceral ganglion, GABA and DA, and GABA and 5-HT were colocalized in the same cell types. Overall, we found that GABAergic cells were most numerous in the cerebral ganglion of H. asinina. Further studies are required to determine the functions of these neurotransmitters in relation to their distribution

A meta-analysis of international tourism demand elasticities

This study uses meta-analysis to examine the relationship between estimated international tourism demand elasticities and the data characteristics and study features that may affect such empirical estimates. By reviewing 195 studies published during the period 1961–2011, the meta-regression analysis shows that origin, destination, time period, modeling method, data frequency, the inclusion/omission of other explanatory variables and their measures, and sample size all significantly influence the estimates of the demand elasticities generated by a model. Moreover, the demand elasticities at both product and destination levels are generalized by statistically integrating previous empirical estimates. The findings of this meta-analysis will be useful wherever an understanding of the drivers of tourism demand is critically important