Odor Source Research Articles

Chronic unpredictable mild stress alters odor hedonics and adult olfactory neurogenesis in mice.

Experiencing chronic stress significantly increases the risk for depression. Depression is a complex disorder with varied symptoms across patients. However, feeling of sadness and decreased motivation, and diminished feeling of pleasure (anhedonia) appear to be core to most depressive pathology. Odorants are potent signals that serve a critical role in social interactions, avoiding danger, and consummatory behaviors. Diminished quality of olfactory function is associated with negative effects on quality of life leading to and aggravating the symptoms of depression. Odor hedonic value (I like or I dislike this smell) is a dominant feature of olfaction and guides approach or avoidance behavior of the odor source. The neural representation of the hedonic value of odorants is carried by the granule cells in the olfactory bulb, which functions to modulate the cortical relay of olfactory information. The granule cells of the olfactory bulb and those of the dentate gyrus are the two major populations of cells in the adult brain with continued neurogenesis into adulthood. In hippocampus, decreased neurogenesis has been linked to development or maintenance of depression symptoms. Here, we hypothesize that chronic mild stress can alter olfactory hedonics through effects on the olfactory bulb neurogenesis, contributing to the broader anhedonia phenotype in stress-associated depression. To test this, mice were subjected to chronic unpredictable mild stress and then tested on measures of depressive-like behaviors, odor hedonics, and measures of olfactory neurogenesis. Chronic unpredictable mild stress led to a selective effect on odor hedonics, diminishing attraction to pleasant but not unpleasant odorants, an effect that was accompanied by a specific decrease in adult neurogenesis and of the percentage of adult-born cells responding to pleasant odorants in the olfactory bulb.

Gain control in olfactory receptor neurons and the detection of temporal fluctuations in odor concentration.

The ability of the cockroach to locate an odor source in still air suggests that the temporal dynamic of odor concentration in the slowly expanding stationary plume alone is used to infer odor source distance and location. This contradicts with the well-established view that insects use the wind direction as the principle directional cue. This contribution highlights the evidence for, and likely functional relevance of, the capacity of the cockroach's olfactory receptor neurons to detect and process-from one moment to the next-not only a succession of odor concentrations but also the rates at which concentration changes. This presents a challenge for the olfactory system because it must detect and encode the temporal concentration dynamic in a manner that simultaneously allows invariant odor recognition. The challenge is met by a parallel representation of odor identity and concentration changes in a dual pathway that starts from olfactory receptor neurons located in two morphologically distinct types of olfactory sensilla. Parallel processing uses two types of gain control that simultaneously allocate different weight to the instantaneous odor concentration and its rate of change. Robust gain control provides a stable sensitivity for the instantaneous concentration by filtering the information on fluctuations in the rate of change. Variable gain control, in turn, enhances sensitivity for the concentration rate according to variations in the duration of the fluctuation period. This efficiently represents the fluctuation of concentration changes in the environmental context in which such changes occur.

A neonicotinoid pesticide alters Drosophila olfactory processing

Neonicotinoid pesticides are well-known for their sublethal effects on insect behavior and physiology. Recent work suggests neonicotinoids can impair insect olfactory processing, with potential downstream effects on behavior and possibly survival. However, it is unclear whether impairment occurs during peripheral olfactory detection, during information processing in central brain regions, or in both contexts. We used Drosophila melanogaster to explore the potential for neonicotinoids to disrupt olfaction by conducting electrophysiological analyses of single neurons and whole antennae of flies exposed to varying concentrations of the neonicotinoid imidacloprid (IMD) that were shown to cause relative differences in fly survival. Our results demonstrated that IMD exposure significantly reduced the activity of a single focal olfactory neuron and delayed the return to baseline activity of the whole antenna. To determine if IMD also impacts olfactory-guided behavior, we compared flies’ relative preference for odor sources varying in ethanol content. Flies exposed to IMD had a greater relative preference for ethanol-laced pineapple juice than control flies, demonstrating that neuronal shifts induced by IMD that we observed are associated with changes in relative preference. Given the interest in the sensory impacts of agrochemical exposure on wild insect behavior and physiology, we highlight the potential of Drosophila as a tractable model for investigating the effects of pesticides at scales ranging from single-neuron physiology to olfactory-guided behavior.

Preen oil composition of Pied Flycatchers is similar between partners but differs between sexes and breeding stages

Preen oil, the secretion of the uropygial gland, may be an important source of body odour in birds. By characterizing the chemical composition of preen oil, we can describe the olfactory phenotypes of birds and investigate whether odours could have a function in sexual signalling or other chemical communication. Here we analysed the preen oil of a wild passerine, the European Pied Flycatcher Ficedula hypoleuca, to find out whether it holds socially relevant information. We sampled both the female and male of breeding pairs during nestling rearing to test for sex differences and within‐pair similarity. We additionally sampled the females during incubation to test for changes across breeding stages and for individual repeatability of chemical profiles. Pair mates had similar chemical profiles in comparison with other breeding adults. Furthermore, we found evidence for sex differences and for changes across breeding stages. Notably, the preen oil of females was more diverse and more volatile than that of males, and the preen oil secreted by females during incubation was more volatile than that secreted during nestling rearing. However, we found no evidence for individual repeatability of chemical profiles across breeding stages in females. Our results point towards a function of preen oil in sexual signalling, although other functions should not be excluded. Our study is a first step towards understanding the role of odours in the social life of an important avian model species used in the study of mate choice and sexual selection.

Research on Improved Quantitative Identification Algorithm in Odor Source Searching Based on Gas Sensor Array.

In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution -1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45° is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point.

Chronic exposure to odors at naturally occurring concentrations triggers limited plasticity in early stages of Drosophila olfactory processing.

In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly Drosophila, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is reencountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. Here, we investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, we found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. We further investigated the DL5 olfactory coding channel and found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment.

Homing and Nest Recognition in Nocturnal Blue Petrels: What Scent May Attract Birds to their Burrows?

Hypogean petrels return to the same nest burrow to breed on remote islands during the summer months. Their nocturnal behavior at the colony, strong musky odor and olfactory anatomy suggest an important role of olfaction in homing behavior and nest recognition. Behavioral experiments showed that olfactory cues are sufficient to allow nest identification, suggesting a stabile chemical signature emanating from burrows and facilitating nest recognition. However, the chemical nature and sources of this odor remain unknown. To better understand the nest odor composition, we analyzed volatile organic compounds (VOCs) of nests of blue petrels (Halobaena caerulea) derived from three different odor sources: nest air, nest material and feather samples. We also compared, during two successive years, VOCs from burrows with an incubating breeder on the nest, and burrows used during the breeding season by blue petrels but shortly temporally unoccupied by breeders. We found that the nest air odor was mainly formed by the owners’ odor, which provided an individual chemical label for nests that appeared stabile over the breeding season. These findings, together with the previous homing behavioral studies showing an essential role of the sense of smell in blue petrels, strongly suggest that the scent emanating from burrows of blue petrels provides the information that facilitates nest recognition and homing.

Smelling danger: Lady beetle odors affect aphid population abundance and feeding, but not movement between plants

Predator-prey interactions are complex ecological interactions that influence community structure and function. Predators affect prey density directly by consuming and removing prey from the environment. Additionally, predators can elicit non-consumptive effects in prey which influence prey survival by altering prey behavior and physiology. Key to understanding the consequences of non-consumptive effects for prey populations is determining mechanisms of detection of predation risk. With insects, olfactory cues are commonly used to convey information. It has been shown that insect prey may eavesdrop on predator odor cues as a means to determine predation risk. Herein, we assessed whether the volatile odors of the predatory insect Harmonia axyridis (multicolored Asian lady beetle) affect the behavior (feeding and dispersal behaviors) and performance (population abundance) of aphids (Myzus persicae). Aphids reduced phloem ingestion when feeding in close proximity to lady beetle cues, however, predator odors did not drive aphids to relocate on plants or to disperse across plants away from the odor source. The lack of dispersal we observed is in contrast to studies that show increased aphid movement in the presence of predators that can make physical contact with the aphids. We also found that lady beetle odor cues had a population-level effect on aphids, with 25% reduction in aphid population abundance in the presence of the odor cues. This study highlights that anti-predator strategies may differ depending on the mechanism of detection of predation risk (i.e. olfactory versus physical stimuli), or the context in which they experience them, and raises the question of whether anti-predator responses observed are maladaptive or beneficial to aphid survival. These conclusions are critical for both our basic understanding of the mechanisms driving predator-prey interactions as well as providing insight into pest-natural enemy interactions within agricultural landscapes.

A Comparison of Multiple Odor Source Localization Algorithms

There are two primary algorithms for autonomous multiple odor source localization (MOSL) in an environment with turbulent fluid flow: Independent Posteriors (IP) and Dempster–Shafer (DS) theory algorithms. Both of these algorithms use a form of occupancy grid mapping to map the probability that a given location is a source. They have potential applications to assist in locating emitting sources using mobile point sensors. However, the performance and limitations of these two algorithms is currently unknown, and a better understanding of their effectiveness under various conditions is required prior to application. To address this knowledge gap, we tested the response of both algorithms to different environmental and odor search parameters. The localization performance of the algorithms was measured using the earth mover’s distance. Results indicate that the IP algorithm outperformed the DS theory algorithm by minimizing source attribution in locations where there were no sources, while correctly identifying source locations. The DS theory algorithm also identified actual sources correctly but incorrectly attributed emissions to many locations where there were no sources. These results suggest that the IP algorithm offers a more appropriate approach for solving the MOSL problem in environments with turbulent fluid flow.

Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects.

Odor plume tracking is important for many organisms, and flying insects have served as popular model systems for studying this behavior both in field and laboratory settings. The shape and statistics of the airborne odor plumes that insects follow are largely governed by the wind that advects them. Prior atmospheric studies have investigated aspects of microscale wind patterns with an emphasis on characterizing pollution dispersion, enhancing weather prediction models, and for assessing wind energy potential. Here, we aim to characterize microscale wind dynamics through the lens of short-term ecological functions by focusing on spatial and temporal scales most relevant to insects actively searching for odor sources. We collected and compared near-surface wind data across three distinct environments (sage steppe, forest, and urban) in Northern Nevada. Our findings show that near-surface wind direction variability decreases with increasing wind speeds and increases in environments with greater surface complexity. Across environments, there is a strong correlation between the variability in the wind speed (i.e., turbulence intensity) and wind direction (i.e., standard deviation in wind direction). In some environments, the standard deviation in the wind direction varied as much as 15°-75° on time scales of 1-10 min. We draw insight between our findings and previous plume tracking experiments to provide a general intuition for future field research and guidance for wind tunnel design. Our analysis suggests a hypothesis that there may be an ideal range of wind speeds and environment complexity in which insects will be most successful when tracking odor plumes over long distances.

The Use of Field Olfactometry in the Odor Assessment of a Selected Mechanical–Biological Municipal Waste Treatment Plant within the Boundaries of the Selected Facility—A Case Study

Odor management plans indicate the need to identify odor sources in waste management facilities. Finding the right tool for this type of task is a key element. This article covers a new approach for odor quantification and source identification at a selected waste management facility by coupling field olfactometry and the spatial interpolation method, such as inverse weighted distance. As the results show, this approach works only partially. Field olfactometry seems to be a suitable tool for odor identification that could be an instrument incorporated into odor management plans as it allowed for recognition of most odor-generating places at the selected facility, i.e., waste stabilization area, green waste storage area, and bioreactors. However, spatial distributions obtained by the selected interpolation method are characterized by high errors during cross-validation, and they tend to overestimate odor concentrations. The substantial weakness of the selected interpolation method is that it cannot handle points where the odor concentration is below the detection threshold. Therefore, the usefulness of such a method is questionable when it comes to odor management plans. Since field olfactometry is a reliable tool for odor measurements, further research into computational methods is needed, including advanced interpolation methods or dispersion modeling based on field olfactometry data.

E-Nose design and structures from statistical analysis to application in robotic: a compressive review

Since 1982, the olfactory system of creatures has piqued the interest of academics who seek to create a comparable system. Despite its mysterious nature, the first stage has been successfully completed with the development of the E-nose. Its extended applications have opened new doors for researchers, ranging from food quality testing to bomb detection and even, more recently, identifying those infected with the coronavirus. In this talk, we will review the structure and sensor behavior of the E-nose, as well as its applications, such as odour source localization and various applications in agriculture. The challenge of odour identification has prompted researchers to employ robots with sensors to investigate and locate odour sources. The present study aims to synthesize documented research and provide a fresh perspective on odour localization research efforts and tests conducted. The study highlights previous attempts to equip robots with sensors to explore the real indoor or outdoor environment. Initially, a review was conducted to investigate various aspects of the sector and the obstacles involved.

Estimating odor source proximity via two MOS sensors based on deep learning method

Odor source proximity estimation (OSPE) is essential in many chemical sensing applications, such as chemical leak accident rescue or odor-guided robot navigation. To improve the accuracy and generalization of OSPE, a new odor source proximity indicator (PI) called dual-sensor distance ratio (DR) is proposed in this paper, and an odor source proximity deep neural network (OP-NET) is established, which can estimate the DR by automatically decoding the source proximity information from the signals of two metal oxide semiconductor (MOS) sensors. A dataset is generated by the simulation and experimental platform for the deep learning-based OSPE research, where the simulation data is used for model hyperparameter optimization, while the experiment data is used for OSPE method comparison, crosswind validation, and generalization test. The results show that the best OSPE errors of OP-NET are 0.49 m, 0.69 m and 0.99 m in the downwind, crosswind, generalization test, respectively. The proposed deep learning-based OSPE model is expected to improve the ability of robot to locate odor sources. © 2001 Elsevier Science. All rights reserved.

Optimal active particle navigation meets machine learning (a)

The question of how “smart” active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale, and give a perspective by discussing some of the challenges which are ahead of us. Besides exemplifying an elementary approach to optimal navigation problems, the article focuses on works utilizing machine learning-based methods. Such learning-based approaches can uncover highly efficient navigation strategies even for problems that involve, e.g., chaotic, high-dimensional, or unknown environments and are hardly solvable based on conventional analytical or simulation methods.

Active Sensing in Bees Through Antennal Movements Is Independent of Odor Molecule.

When sampling odors, many insects are moving their antennae in a complex but repeatable fashion. Previous studies with bees have tracked antennal movements in only two dimensions, with a low sampling rate and with relatively few odorants. A detailed characterization of the multimodal antennal movement patterns as function of olfactory stimuli is thus wanted. The aim of this study is to test for a relationship between the scanning movements and the properties of the odor molecule. We tracked several key locations on the antennae of bumblebees at high frequency and in three dimensions while stimulating the insect with puffs of 11 common odorants released in a low-speed continuous flow. Water and paraffin were used as negative controls. Movement analysis was done with the neural network Deeplabcut. Bees use a stereotypical oscillating motion of their antennae when smelling odors, similar across all bees, independently of the identity of the odors and hence their diffusivity and vapor pressure. The variability in the movement amplitude among odors is as large as between individuals. The main type of oscillation at low frequencies and large amplitude is triggered by the presence of an odor and is in line with previous work, as is the speed of movement. The second oscillation mode at higher frequencies and smaller amplitudes is constantly present. Antennae are quickly deployed when a stimulus is perceived, decorrelate their movement trajectories rapidly, and oscillate vertically with a large amplitude and laterally with a smaller one. The cone of airspace thus sampled was identified through the 3D understanding of the motion patterns. The amplitude and speed of antennal scanning movements seem to be function of the internal state of the animal, rather than determined by the odorant. Still, bees display an active olfactory sampling strategy. First, they deploy their antennae when perceiving an odor. Second, fast vertical scanning movements further increase the odorant capture rate. Finally, lateral movements might enhance the likelihood to locate the source of odor, similarly to the lateral scanning movement of insects at odor plume boundaries.

Odorous Substances in Urban Drainage Pipelines and the Removal Technology: A Review

The existence of odors in drainage pipelines is one of the most prominent environmental problems that urban residents complain about nowadays. Odorous substances in sewage can cause corrosion and erosion in drainage pipelines, and even lead to great harm to the human body and environments. Ideas for in situ odor control can be divided into two main categories: the elimination of odorous substances and the inhibition of the production of odorous substances. However, there is a lack of comprehensive summary of in situ overall deodorization techniques, which has limited the wide application of these methods. We conducted a systematic review to summarize recent advances in in situ overall deodorization. Firstly, the main odorous substances in drainage pipelines and their basic characteristics are concluded. Special attention has been paid to volatile sulfur compounds (VSCs) and nitrogen-containing compounds, as the main odorous substances. Subsequently, typical sources of these odorous substances are summarized based on their formation mechanisms. Then, in situ deodorization techniques (including pipeline condition optimization techniques, odor source control techniques, chemical control techniques, and biological control techniques) are introduced. Finally, upcoming research efforts on deodorization mechanism improvement, research gap supplementation, and economic efficiency enhancement to meet practical conditions are proposed.

A parosmia severity index based on word-classification predicts olfactory abilities and impairment

Parosmia is an olfactory disorder that involves distortions of specific odors that may co-occur with anosmia, loss of smell of other odors. Little is known about which odors frequently trigger parosmia, and measures of parosmia severity are lacking. Here, we present an approach to understand and diagnose parosmia that is based on semantic properties (e.g., valence) of words describing odor sources (“fish”, “coffee”, etc.). Using a data-driven method based on natural language data, we identified 38 odor descriptors. Descriptors were evenly dispersed across an olfactory-semantic space, which was based on key odor dimensions. Parosmia patients (n = 48) classified the corresponding odors in terms of whether they trigger parosmic or anosmic sensations. We investigated whether these classifications are related to semantic properties of the descriptors. Parosmic sensations were most often reported for words describing unpleasant odors of inedibles that are highly associated to olfaction (e.g., “excrement”). Based on PCA modeling, we derived the Parosmia Severity Index—a measure of parosmia severity that can be determined solely from our non-olfactory behavioral task. This index predicts olfactory-perceptual abilities, self-reported olfactory impairment, and depression. We thus provide a novel approach for investigating parosmia and establishing its severity that does not require odor exposure. Our work may enhance our understanding of how parosmia changes over time and how it is expressed differently across individuals.

Flare exhaust: An underestimated pollution source in municipal solid waste landfills

Flares are commonly used in municipal solid waste landfills, and the pollution from flare exhaust is usually underestimated. This study aimed to reveal the odorants, hazardous pollutants, and greenhouse gas emission characteristics of the flare exhaust. Odorants, hazardous pollutants, and greenhouse gases emitted from air-assisted flares and a diffusion flare were analyzed, the priority monitoring pollutants were identified, and the combustion and odorant removal efficiencies of the flares were estimated. The concentrations of most odorants and the sum of odor activity values decreased significantly after combustion, but the odor concentration could still exceed 2,000. The odorants in the flare exhaust were dominated by oxygenated volatile organic compounds (OVOCs), while the major odor contributors were OVOCs and sulfur compounds. Hazardous pollutants, including carcinogens, acute toxic pollutants, endocrine disrupting chemicals, and ozone precursors with the total ozone formation potential up to 75 ppmv, as well as greenhouse gases (methane and nitrous oxide with maximum concentrations of 4,000 and 1.9 ppmv, respectively) were emitted from the flares. Additionally, secondary pollutants, such as acetaldehyde and benzene, were formed during combustion. The combustion performance of the flares varied with landfill gas composition and flare design. The combustion and pollutant removal efficiencies could be lower than 90%, especially for the diffusion flare. Acetaldehyde, benzene, toluene, p-cymene, limonene, hydrogen sulfide, and methane could be priority monitoring pollutants for flare emissions in landfills. Flares are useful for odor and greenhouse gas control in landfills, but they are also potential sources of odor, hazardous pollutants, and greenhouse gases.

Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark.

The olfactory search POMDP (partially observable Markov decision process) is a sequential decision-making problem designed to mimic the task faced by insects searching for a source of odor in turbulence, and its solutions have applications to sniffer robots. As exact solutions are out of reach, the challenge consists in finding the best possible approximate solutions while keeping the computational cost reasonable. We provide a quantitative benchmarking of a solver based on deep reinforcement learning against traditional POMDP approximate solvers. We show that deep reinforcement learning is a competitive alternative to standard methods, in particular to generate lightweight policies suitable for robots.

Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality.

Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume's composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.