Investigation Of Leucine And Glutamic Acid Properties From The Nanoscale To The Macroscale

Aerosols are known to have important effects on climate, the atmosphere, and human health. The extent of those effects is unknown and largely depend on the interaction of aerosols with water in the atmosphere. Ambient aerosols are complex mixtures of both inorganic and organic compounds. The cloud condensation nuclei (CCN) activities, hygroscopic behavior and particle morphology of a monocarboxylic amino acid (leucine) and a dicarboxylic amino acid (glutamic acid) were investigated. Activation diameters at various supersaturation conditions were experimentally determined and compared with Köhler theoretical values. The theory accounts for both surface tension and the limited solubility of organic compounds. It was discovered that glutamic acid aerosols readily took on water both when relative humidity was less than 100% and when the supersaturation condition was reached, while leucine did not show any water activation at those conditions. Moreover, the study also suggests that Köhler theory describes CCN activity of organic compounds well when only surface tension of the compound is taken into account and complete solubility is assumed. Single parameter ¿ was also computed using both CCN data and hygroscopic growth factor (GF). The results of ¿ range from 0.17 to 0.53 using CCN data and 0.09 to 0.2 using GFs. Finally, the study suggests that during the water-evaporation/particle-nucleation process, crystallization from solution droplets takes place at different locations: for glutamic acid at the particles¿ center and leucine at the particles¿ boundary.

Multimodal Integration in Statistical Learning: Evidence from the McGurk Illusion

Recent advances in the field of statistical learning have established that learners are able to track regularities of multimodal stimuli, yet it is unknown whether the statistical computations are performed on integrated representations or on separate, unimodal representations. In the present study, we investigated the ability of adults to integrate audio and visual input during statistical learning. We presented learners with a speech stream synchronized with a video of a speaker's face. In the critical condition, the visual (e.g., /gi/) and auditory (e.g., /mi/) signals were occasionally incongruent, which we predicted would produce the McGurk illusion, resulting in the perception of an audiovisual syllable (e.g., /ni/). In this way, we used the McGurk illusion to manipulate the underlying statistical structure of the speech streams, such that perception of these illusory syllables facilitated participants' ability to segment the speech stream. Our results therefore demonstrate that participants can integrate audio and visual input to perceive the McGurk illusion during statistical learning. We interpret our findings as support for modality-interactive accounts of statistical learning.