SmellSpace: An Odor-Based Social Network as a Platform for Collecting Olfactory Perceptual Data.

A common goal in olfaction research is modeling the link between odorant structure and odor perception. Such modeling efforts require large data sets on olfactory perception, yet only a few of these are publicly and freely available. Given that individual odor perception may be informative on personal makeup and interpersonal relationships, we hypothesized that people would gladly provide olfactory perceptual estimates in the context of an odor-based social network. We developed a web-based infrastructure for such a network we called SmellSpace and distributed 10 000 scratch-and-sniff registration booklets each containing a subset of 12 out of 35 microencapsulated odorants. Within ~100 days, we obtained data from ~1000 participants who rated the odorants along 13 verbal descriptors. To verify that these estimates are comparable to lab-collected estimates we tested 26 participants in a controlled lab setting using the same odorants and descriptors. We observed remarkably high overall group correlations between lab and SmellSpace data, implying that this method provides for credible group-representations of odorants. We further estimated the usability of the data by applying to it two previously published models that used odorant structure alone to predict either odorant pleasantness or pairwise odorant perceptual similarity. We observed statistically significant predictions in both cases, thus further implying that the current data may be helpful toward future efforts of modeling olfactory perception from structure. We conclude that an odor-based social network is a potentially useful instrument for collecting extensive data on olfactory perception and here post the complete raw data set from the first ~1000 participants.

A Cross Modal Performance-Based Measure of Sensory Stimuli Intricacy.

We define a new measure of sensory stimuli which has the following properties: It is cross modal, performance based, robust, and well defined. We interpret this measure as the intricacy or complexity of the stimuli, yet its validity is independent of its interpretation. We tested the validity and cross modality of our measure using three olfactory and one visual experiment. In order to test the link between our measure and cognitive performance we also conducted an additional visual experiment. We found that our measure is correlated with the results of the well-established Rapid Serial Visual Presentation masking experiment. Specifically, ranking stimuli according to our measure was correlated at r = 0.75 (p < 0.002) with masking effectiveness. Thus, our novel measure of sensory stimuli provides a new quantitative tool for the study of sensory processing.

Predicting odor perceptual similarity from odor structure.

To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction.

Perceptual convergence of multi-component mixtures in olfaction implies an olfactory white.

In vision, two mixtures, each containing an independent set of many different wavelengths, may produce a common color percept termed "white." In audition, two mixtures, each containing an independent set of many different frequencies, may produce a common perceptual hum termed "white noise." Visual and auditory whites emerge upon two conditions: when the mixture components span stimulus space, and when they are of equal intensity. We hypothesized that if we apply these same conditions to odorant mixtures, "whiteness" may emerge in olfaction as well. We selected 86 molecules that span olfactory stimulus space and individually diluted them to a point of about equal intensity. We then prepared various odorant mixtures, each containing various numbers of molecular components, and asked human participants to rate the perceptual similarity of such mixture pairs. We found that as we increased the number of nonoverlapping, equal-intensity components in odorant mixtures, the mixtures became more similar to each other, despite not having a single component in common. With ~30 components, most mixtures smelled alike. After participants were acquainted with a novel, arbitrarily named mixture of ~30 equal-intensity components, they later applied this name more readily to other novel mixtures of ~30 equal-intensity components spanning stimulus space, but not to mixtures containing fewer components or to mixtures that did not span stimulus space. We conclude that a common olfactory percept, "olfactory white," is associated with mixtures of ~30 or more equal-intensity components that span stimulus space, implying that olfactory representations are of features of molecules rather than of molecular identity.

Immune-induced evolutionary selection focused on a single reading frame in overlapping hepatitis B virus proteins.

Viruses employ various means to evade immune detection. Reduction of CD8(+) T cell epitopes is one of the common strategies used for this purpose. Hepatitis B virus (HBV), a member of the Hepadnaviridae family, has four open reading frames, with about 50% overlap between the genes they encode. We computed the CD8(+) T cell epitope density within HBV proteins and the mutations within the epitopes. Our results suggest that HBV accumulates escape mutations that reduce the number of epitopes. These mutations are not equally distributed among genes and reading frames. While the highly expressed core and X proteins are selected to have low epitope density, polymerase, which is expressed at low levels, does not undergo the same selection. In overlapping regions, mutations in one protein-coding sequence also affect the other protein-coding sequence. We show that mutations lead to the removal of epitopes in X and surface proteins even at the expense of the addition of epitopes in polymerase. The total escape mutation rate for overlapping regions is lower than that for nonoverlapping regions. The lower epitope replacement rate for overlapping regions slows the evolutionary escape rate of these regions but leads to the accumulation of mutations more robust in the transfer between hosts, such as mutations preventing proteasomal cleavage into epitopes.