Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem

<div><p>Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).</p></div

Odour Network.

<p><b>(a-g)</b> The communities detected in the odour network of databases using modularity maximization algorithm. The colours indicate the different communities.</p

Network Characteristics of the odour network and the comparison with random network where, A = Avg degree, N_d = Network diameter, N_l = Avg path length, D_g = Graph density, α = Power law exponent, X_min = Power law cutoff degree, r = Assortativity Coefficient, cl^avg = Clustering Coefficient and <i>R-cl</i>^<i>avg</i> = Random Clustering Coefficient.

<p>Network Characteristics of the odour network and the comparison with random network where, A = Avg degree, N_d = Network diameter, N_l = Avg path length, D_g = Graph density, α = Power law exponent, X_min = Power law cutoff degree, r = Assortativity Coefficient, cl^avg = Clustering Coefficient and <i>R-cl</i>^<i>avg</i> = Random Clustering Coefficient.</p

Natural transformation efficiency of <i>H. pylori</i> strains AM5 and SS1 and their respective <i>hpyAVIBM</i> deletion mutant strains.

<p>Values are calculated as transformants/cfu/mg DNA. P<0.0001.</p

Semantic analysis and comparison of the odour network using the brown database. The networks have been created using a bag of words approach using window sizes of 2,3,4 according to the average number of perceptual descriptors per molecule in each database. The odour subnetworks consisted of only those perceptual descriptors that were found in the semantic database. Network Characteristics along with Eigen value similarity of the perceptual network in comparison with random network has been tabulated, where, N = Number of nodes, WE = Number of Weighted Edges, A = Avg degree, N_d = Network diameter, N_l = Avg path length, D_g = Graph density, r = Assortativity Coefficient and cl^avg = Clustering Coefficient

<p>Semantic analysis and comparison of the odour network using the brown database. The networks have been created using a bag of words approach using window sizes of 2,3,4 according to the average number of perceptual descriptors per molecule in each database. The odour subnetworks consisted of only those perceptual descriptors that were found in the semantic database. Network Characteristics along with Eigen value similarity of the perceptual network in comparison with random network has been tabulated, where, N = Number of nodes, WE = Number of Weighted Edges, A = Avg degree, N_d = Network diameter, N_l = Avg path length, D_g = Graph density, r = Assortativity Coefficient and cl^avg = Clustering Coefficient</p

Motility assay.

<p>The photograph shows a BHI 7% FBS soft agar plate after 4 days of incubation. (<b>A</b>) SS1 (<b>B</b>) AM5 (<b>C</b>) Graph showing relative change in the diameter of mutant vs wild-type strain.</p

Database characteristics:

<p>The number of perceptual descriptors vs number of molecules per database.</p

Degree Distribution.

<p>The degree distribution of the networks in log-log plot along with the fitted truncated power law. For all the networks, except SuperScent, the probability that a given perceptual descriptor connects with k other perceptual descriptors follows a power law (p(x) = x^-α) with α Є [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141263#pone.0141263.ref002" target="_blank">2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141263#pone.0141263.ref003" target="_blank">3</a>] or α ~ 2.</p

Western blotting for CagA and VacA protein levels in <i>H. pylori</i> strainsAM5 and AM5Δ<i>hpyAVIBM</i>.

<p>Western blotting for CagA and VacA protein levels in <i>H. pylori</i> strainsAM5 and AM5Δ<i>hpyAVIBM</i>.</p

Hubs in the Network.

<p>Hubs in the Network.</p