Biology subjects

Mainland, J. D.

Publications and source records attributed to Mainland, J. D..

16 recordsLinked to original sources

bioRxiv2026

Odors Smell Like Their Components: A Linear Framework for Predicting Olfactory Mixture Perception

Most smells, whether in food, perfume, or the environment, are complex mixtures of many odor molecules. A principled framework for modeling natural mixtures has been considered intractable because mixtures are widely assumed to generate nonlinear perceptual interactions that mirror nonlinear responses at the receptor and neural levels. We quantified perceptual interactions across 432 mixtures composed of 144 component odorants using a trained human panel, and nearly all mixtures were explained by a linear average of their component quality profiles. Within this linear modeling framework, averaging component profiles predicted mixture perception significantly better than the previous state-of-the-art and approached the ceiling set by measurement noise. Even mixtures previously reported to exhibit emergence were equally well predicted. These results suggest that the main challenge in modeling odor perception is characterizing individual components, rather than exhaustively mapping odor-odor interactions. Just as additive color mixing enabled colorimetry, linear mixing in olfaction opens the door to quantitative odorimetry, allowing mixtures to be predicted, reconstructed, and optimized computationally.

neuroscience↗

bioRxiv2026

Odor Sampling Bags Enable Reliable Delivery of Controlled Odor Concentrations

Precise control of odorant concentration is essential for reliable olfactory research, yet existing odorant delivery methods often suffer from solvent interactions and dilution from ambient air, limiting stimulus consistency in olfactory research. We developed an odor sampling bag system using Nalophan plastic to create a closed headspace with air as the carrier medium, eliminating solvent-related variability and ambient air dilution. In two independent experiments, 15 trained panelists each rated the perceived intensity of seven concentrations of benzaldehyde and 2-heptanone using both gas-sampling bags and glass jars. Bags produced higher maximum perceptual intensities (p < 0.001) and greater test-retest reliability than jars (Experiment 1: r = 0.89 vs. 0.81, p < 0.001; Experiment 2: r = 0.86 vs. 0.72, p < 0.001). Notably, the two tested odorants showed different maximum intensities in bags (p < 0.001) but not jars (p = 0.85), suggesting bags better preserve odorant-specific concentration differences. Photoionization detector measurements confirmed stable headspace concentrations over time, comparable to industry-standard Tedlar bags. This cost-effective approach offers improved stimulus control for olfactory psychophysics research.

neuroscience↗

bioRxiv2025

High-Fidelity Tuning of Olfactory Mixture Distances in the Perceptual Space of Smell Through a Community Effort

A central goal in sensory science is to establish quantitative mappings between physical stimuli and perceptual responses. While such mappings are well characterized in vision and audition, they remain poorly defined in olfaction, limiting progress toward understanding the representations of smell. Predicting perceptual similarity between odor mixtures offers a promising route to formalize these relationships. To advance this effort, the DREAM (Dialogue for Reverse Engineering Assessment and Methods) Olfactory Mixtures Prediction Challenge assembled a curated, cross-study dataset describing the similarity of 507 mixture pairs and an unpublished test set of 46 mixture pairs. Teams competed to predict the perceptual similarity of mixture pairs, and then collaborated post-challenge to create an ensemble combining top-performing models that notably improves predictions over the existing state-of-the-art models. Moreover, ensemble model maintains high predictive accuracy in novel validation set. Our model provides a reproducible framework for neuroscientists, chemists, and engineers to compare odor mixtures and provides a foundation for future efforts towards better understanding the olfactory properties of mixtures.

neuroscience↗

bioRxiv2025

Mice and humans evaluate odor stimulus strength using common psychophysical principles

Sensory systems translate physical stimuli from the environment--such as light, sound, or chemicals--into signals that the brain can interpret. Across these systems, the amplitude of a stimulus is represented by its perceived intensity. Although previous research has extensively studied how the brain represents physical stimuli, less is known about how it represents perceptual variables such as stimulus intensity. This is primarily due to the difficulty in measuring perceptual responses in animal models, where neural recordings are more accessible. In this study, we use mouse olfaction as a model system to develop a framework for measuring perceived odor intensity. We begin by employing a two-odor concentration classification task to demonstrate that both mice and humans assess stimulus amplitude using a common perceptual scale. We then show that this scale corresponds to intensity. Finally, we apply this method to determine isointense concentrations of different odorants in mice. Our approach offers a powerful tool for testing hypotheses about the neural mechanisms underlying perceived odor intensity, potentially enhancing our understanding of olfactory processing and its neural substrates.

neuroscience↗

bioRxiv2025

A quantitative framework for predicting odor intensity across molecule and mixtures

In vision and hearing, standardized units such as lumens (for brightness) and decibels (for loudness) allow consistent quantification of stimulus intensity, enabling precise control of sensory experiences. Olfaction, by contrast, currently lacks a robust quantitative framework linking physical stimulus properties directly to perceived odor intensity, complicating efforts to accurately characterize and manipulate aromas. To bridge this gap, we used a precisely controlled odor delivery system combined with deep learning models to predict the intensity of both single molecules and mixtures from physical properties. These models allowed us to develop an automated, quantitative method that accurately identifies which volatile components meaningfully contribute to aroma perception, overcoming the limitations of traditional heuristic approaches such as odor activity values and demonstrating practical utility in complex naturalistic odors.

neuroscience↗

bioRxiv2022

Pyrfume: A Window to the World's Olfactory Data

Advances in theoretical understanding are frequently unlocked by access to large, diverse experimental datasets. Olfactory neuroscience and psychophysics remain years behind the other senses in part because rich datasets linking olfactory stimuli with their corresponding percepts, behaviors, and neural pathways underlying this transformation, remain scarce. Here we present a concerted effort to unlock and unify dozens of stimulus-linked olfactory datasets across species and modalities under a unified framework called Pyrfume. We present examples of how researchers might use Pyrfume to conduct novel analyses uncovering new principles, introduce trainees to the field, or construct benchmarks for machine olfaction.

neuroscience↗

bioRxiv2022

A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

Mapping molecular structure to odor perception is a key challenge in olfaction. Here, we use graph neural networks (GNN) to generate a Principal Odor Map (POM) that preserves perceptual relationships and enables odor quality prediction for novel odorants. The model is as reliable as a human in describing odor quality: on a prospective validation set of 400 novel odorants, the model-generated odor profile more closely matched the trained panel mean (n=15) than did the median panelist. Applying simple, interpretable, theoretically-rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors. One-Sentence SummaryAn odor map achieves human-level odor description performance and generalizes to diverse odor-prediction tasks.

neuroscience↗

bioRxiv2021

The anatomical logic of smell

The sense of smell helps us navigate the environment, but its molecular architecture and underlying logic remain unknown. The spatial location of odorant receptor genes (Olfrs) in the nose is widely thought to be independent of the structural diversity of the odorants they detect. Using spatial transcriptomics, we created a genome-wide 3D atlas of the mouse olfactory mucosa (OM). Topographic maps of genes differentially expressed in space reveal that both Olfrs and non-Olfrs are distributed in a continuous and overlapping fashion over five broad zones in the OM. The spatial locations of Olfrs correlate with the mucus solubility of the odorants they recognize, providing direct evidence for the chromatographic theory of olfaction. This resource resolved the molecular architecture of the mouse OM, and will inform future studies on mechanisms underlying Olfr gene choice, axonal pathfinding, patterning of the nervous system, and basic logic for the peripheral representation of smell.

neuroscience↗

bioRxiv2021

From musk to body odor: decoding olfaction through genetic variation

The olfactory system combines input from multiple receptor types to represent odor information, but there are few explicit examples relating olfactory receptor (OR) activity patterns to odor perception. To uncover these relationships, we performed genome-wide scans on odor-perception phenotypes for ten odors in 1003 Han Chinese and validated results for six of these odors in an ethnically diverse population (n=364). In both populations, we replicated three previously reported associations ({beta}-ionone/OR5A, androstenone/OR7D4, cis-3-hexen-1-ol/OR2J3 LD-band), suggesting that olfactory phenotype/genotype studies are robust across populations. Two novel associations between an OR and odor perception contribute to our understanding of olfactory coding. First, we found a SNP in OR51B2 that associated with trans-3-methyl-2-hexenoic acid, a key component of human underarm odor. Second, we found two linked SNPs associated with the musk Galaxolide in a novel musk receptor, OR4D6, which is also the first OR shown to drive specific anosmia to a musk compound. We also found that the derived alleles of the SNPs reportedly associated with odor perception tend to reduce odor intensity, supporting the hypothesis that the primate olfactory gene repertoire has degenerated over time. This study provides information about coding for human body odor, and gives us insight into broader mechanisms of olfactory coding, such as how differential OR activation can converge on a similar percept.

genetics↗

bioRxiv2021

The perception of odor pleasantness is shared across cultures

Human sensory experience varies across the globe. Nonetheless, all humans share sensory systems with a common anatomical blueprint. In olfaction, it is unknown to what degree sensory perception, in particular the perception of odor pleasantness, is dictated by universal biological principles versus sculpted by culture. To address this issue, we asked 235 individuals from 9 diverse non-western cultures to rank the hedonic value of monomolecular odorants. We observed substantial global consistency, with molecular identity explaining 41% of the variance in individual pleasantness rankings, while culture explained only 6%. These rankings were predicted by the physicochemical properties of out-of-sample molecules and out-of-sample pleasantness ratings given by a separate group of industrialized western urbanites, indicating human olfactory perception is strongly constrained by universal principles.

animal behavior and cognition↗

bioRxiv2020

Drawing the Borders of Olfactory Space

In studies of vision and audition, stimuli can be chosen to span the visible or audible spectrum; in olfaction, the axes and boundaries defining the analogous odorous space are unknown. As a result, the population of olfactory space is likewise unknown, and anecdotal estimates of 10,000 odorants have endured. The journey a molecule must take to reach olfactory receptors (OR) and produce an odor percept suggests some chemical criteria for odorants: a molecule must be 1) volatile enough to enter the air phase, 2) non-volatile and hydrophilic enough to sorb into the mucous layer coating the olfactory epithelium, 3) hydrophobic enough to enter an OR binding pocket, and 4) activate at least one OR. Here, we developed a simple and interpretable quantitative model that reliably predicts if a molecule is odorous or odorless based solely on the first three criteria. Applying our model to a database of all possible small organic molecules, we estimate that over 30 billion possible compounds are odorous, 6 orders of magnitude larger than current estimates of 10,000. With this model in hand, we can define the boundaries of olfactory space in terms of molecular volatility and hydrophobicity, enabling representative sampling of olfactory stimulus space.

neuroscience↗

bioRxiv2020

Deconstructing the mouse olfactory percept through an olfactory ethological atlas

Odor perception in non-humans is poorly understood. Here, we generated the most comprehensive murine olfactory ethological atlas to date, consisting of behavioral responses to a diverse panel of 73 odorants, including 12 at multiple concentrations. These data revealed that the mouse behavior is incredibly diverse, and changes in response to odor identity and intensity. Using only behavioral responses, ~30% of the 73 odorants could be identified with high accuracy (>96%) by a trained classifier. Mouse behavior occupied a low-dimensional space, consistent with analyses of human olfactory perception. While mouse olfactory behavior is difficult to predict from the corresponding human olfactory percept, three fundamental properties are shared: odor valence is the primary axis of olfactory perception; the physicochemical properties of odorants can predict the olfactory percept; and odorant concentration quantitatively and qualitatively impacts olfactory perception. These results provide a template for future comparative studies of olfactory percepts among species.

neuroscience↗

bioRxiv2019

Sensory nutrition: The role of taste in the reviews of commercial food products

Many factors play a role in choosing what to eat or drink. Here we explored the role of sensation to explain these differences, drawing on consumer reviews for commercially available food products sold through an online retailer. We analyzed 393,568 unique food product reviews from Amazon customers with a total of 256,043 reviewers rating 67,553 products. Taste-associated words were mentioned more than words associated with cost, food texture, customer service, nutrition, smell, or those referring to the trigeminal senses, e.g., spicy. We computed the overall number of reviews that mentioned taste qualities: the word taste was mentioned in over 30% of the reviews (N= 142,768), followed by sweet (10.7%, N=42,315), bitter (2.9%, N=11,424), sour (2.1%, N=8,252), and salty (1.4%, N=5,688). We identified 38 phrases used to describe the evaluation of sweetness, finding that too sweet was used in nearly 0.8% of the reviews and oversweetness was mentioned over 25 times more often than under-sweetness. We then focused on polarizing products, those that elicited a wide difference of opinion (as measured by the ranges of the product ratings). Using the products that had more than 50 reviews, we identified the top 10 most polarizing foods (i.e., those with the largest standard deviation in ratings) and provide representative comments about the polarized taste experience of consumers. Overall, these results support the primacy of taste in real-world food ratings and individualized taste experience, such as whether a product is too sweet. Analysis of consumer review data sets can provide information about purchasing decisions and customer sensory responses to particular commercially available products and represents a promising methodology for the emerging field of sensory nutrition.

neuroscience↗

bioRxiv2018

A competitive binding model predicts nonlinear responses of olfactory receptors to complex mixtures

In color vision, the quantitative rules for mixing lights to make a target color are well understood. By contrast, the rules for mixing odorants to make a target odor remain elusive. A solution to this problem in vision relied on characterizing receptor responses to different wavelengths of light and subsequently relating these responses to perception. In olfaction, experimentally measuring receptor responses to a representative set of complex mixtures is intractable due to the vast number of possibilities. To meet this challenge, we develop a biophysical model that predicts mammalian receptor responses to complex mixtures using responses to single odorants. The dominant nonlinearity in our model is competitive binding (CB): only one odorant molecule can attach to a receptor binding site at a time. This simple framework predicts receptor responses to mixtures of up to twelve monomolecular odorants to within 15% of experimental observations and provides a powerful method for leveraging limited experimental data. Simple extensions of our model describe phenomena such as synergy, overshadowing, and inhibition. We demonstrate that the presence of such interactions can be identified via systematic deviations from the competitive binding model.

neuroscience↗

bioRxiv2017

Genetic variation across the human olfactory receptor repertoire alters odor perception

The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. Genetic variation in a single OR frequently associated with odorant perception, and we validated 10 cases in which in vitro OR function correlated with in vivo odorant perception using a functional assay. This more than doubles the published examples of this phenomenon. For eight of these 10 cases, reduced receptor function associated with reduced intensity perception. In addition, we used participant genotypes to quantify genetic ancestry and found that, in combination with single OR genotype, age and gender, we can explain between 10 and 20% of the perceptual variation in 15 olfactory phenotypes, highlighting the importance of single OR genotype, ancestry, and demographic factors in variation of olfactory perception.

genetics↗

bioRxiv2016

Reverse-engineering human olfactory perception from chemical features of odor molecules

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (\"garlic\", \"fish\", \"sweet\", \"fruit\", \"burnt\", \"spices\", \"flower\", \"sour\"). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.\n\nOne Sentence SummaryResults of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.

neuroscience↗