Search Results (58)

Lampreys are primitive fish that rely significantly on olfactory cues throughout their complex life cycle. The olfactory system of the sea lamprey (Petromyzon marinus) is among the best characterized in vertebrates. In recent decades, tremendous advances have been made by isolating individual compounds from sea lampreys that can replicate natural behavior when artificially applied in the wild. In no other aquatic vertebrate has the olfactory ecology been described in such extensive detail. In the first section, we provide a comprehensive review of olfactory behaviors induced by specific, individual odorants during every major developmental stage of the sea lamprey in behavioral contexts such as feeding, predator avoidance, and reproduction. Moreover, pheromonal inputs have been shown to induce neuroendocrine responses through the hypothalamic-pituitary-gonadal axis, triggering remarkable developmental and physiological effects, such as gametogenesis and increased pheromone release. In the second section of this review, we describe a hypothetical endocrine signaling pathway through which reproductive fitness is increased following pheromone detection. In the final section of this review, we focus on the neuronal circuits that transform olfactory inputs into motor output. We describe specific brain signaling pathways that underlie odor-evoked locomotion. Furthermore, we consider possible modulatory inputs to these pathways that may induce plasticity in olfactory behavior following changes in the external or internal environment. As a whole, this review synthesizes previous and recent progress in understanding the behavioral, endocrine, and neuronal responses of lampreys to chemosensory signals. Full article

Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards. Full article

Diagnosis and intervention at the earliest stages of cancer are imperative for maximizing patient recovery outcomes and substantially increasing survival rates and quality of life. Recently, to facilitate cancer diagnosis, volatile organic compounds (VOCs) have shown potential with unique characteristics as cancer biomarkers. Various insects with sophisticated sensitivities of odor can be quickly and readily trained to recognize such VOCs using olfactory-linked skills. Furthermore, the approach to analyzing VOCs can be made using electronic noses, commonly referred to as e-noses. Using analytical instruments like GC-MS, LC-MS/MS, etc., chemical blends are separated into their constituent parts. The significance of odorant receptors in triggering neural responses to ambient compounds has received great attention in the last twenty years, particularly in the investigation of insect olfaction. Sensilla, a sophisticated olfactory neural framework, is regulated by a neuronal receptor composed of neuronal, non-neuronal, extracellular lymphatic fluid with an effectively generated shell. This review provides an in-depth exploration of the structural, functional, and signaling mechanisms underlying odorant sensitivities and chemical odor detection in the excretory products of cancer patients, addressing current challenges in VOC-based cancer diagnostics and innovative strategies for advancement while also envisioning the transformative role of artificial olfactory systems in the future of cancer detection. Furthermore, the article emphasizes recent preclinical and clinical advancements in VOC applications, highlighting their potential to redefine early diagnostic approaches in oncology. Full article

Monitoring the quality of engine oil improves engine efficiency and reduces engine maintenance costs. Several methods have been proposed for this purpose; however, most of them take too long to test oil quality. This paper introduces a fast, simple, and accurate method to determine oil quality using an electronic nose and artificial intelligence. The TU5 engine and 10-40W “Behran Super Pishtaz” engine oil were used in the experiments. Tests were conducted at six different quality levels. Oil properties such as viscosity, density, flash point, and freezing point were measured at each level. Additionally, oil smell signals were recorded using an olfactory machine at these quality levels. The fraction method was employed to adjust the sensors’ responses. Five statistical features were extracted from each signal, and these features were used to train and test a support vector machine (SVM) for classifying oil quality using the five-fold cross-validation method. The results indicated a statistically significant change in viscosity and density with variations in oil quality. The density increased as the quality decreased. Viscosity, however, initially decreased and then increased at later stages. An analysis of the sensory outputs revealed that changes in oil quality also affected these outputs, with the most pronounced sensitivity observed in the MQ135 and MQ138 sensors. The final accuracies of the SVM in classifying oil quality were 68.22%, 85.86%, and 95.44% for linear, radial basis function (RBF), and polynomial kernels, respectively. The SVM sensitivities for oil qualities A, B, C, D, E, and F were 97.99%, 97.37%, 95.51%, 92.67%, 94.48%, and 94.59%, respectively. Full article

Forest environments have been demonstrated to promote human health and well-being through rich sensory experiences. However, the mechanisms by which olfactory experience affects visitors’ mental health remain to be thoroughly researched, and discussions on emotional resilience, a key competency affecting an individual’s mental health, are particularly rare. To address the challenges of high subjectivity, difficulty in quantifying, and high context-dependency of olfactory experience and emotional resilience in such studies, large language models were introduced to study the National Forest Parks in China and analyse massive user-generated data. This provided new possibilities for constructing a more comprehensive theoretical paradigm of olfactory experience–emotional resilience. The findings indicate that olfactory experiences in National Forest Parks exert a substantial influence on tourists’ emotional resilience, with diverse olfactory experiences demonstrating a more pronounced impact on emotional resilience compared to a single type of olfactory experience. However, this impact exhibits an inverted U-shaped relationship. Natural environment olfactory experiences were found to be more conducive to attention restoration, while artificial environment olfactory experiences were more likely to induce nostalgic feelings. This study found that nostalgic feelings significantly mediated the relationship between artificial environment olfactory experience and emotional resilience, while attention restoration did not significantly mediate the relationship between natural environment olfactory experience and emotional resilience. This provides a novel perspective on the examination of the complex relationship between forest environments, olfactory experience, and emotional resilience. Semantic analyses revealed the complexity and network characteristics of olfactory experiences in National Forest Parks, and at the same time identified four main types of olfactory experiences and scenarios. This research offers valuable insights for forest recreation and leisure management, as well as public health policy development. Full article

The photoperiod has been considered to be a key environmental factor in sheep reproduction, and some studies have shown that the thyroid gland plays an important role in mammalian reproduction, but the molecular mechanism is still unclear. In this study, we used the artificial-light-controlled, ovariectomized, and estradiol-treated model (OVX + E2 model); healthy and consistent 2–3-year-old Sunite multiparous ewes were collected; and thyroids were collected for testing, combined with RNA-seq technology and bioinformatics analysis, to analyze the effects of different photoperiods (long photoperiod treatment for 42 days, LP42; short photoperiod treatment for 42 days, SP42; SP42 transferred to LP42, SPLP42) on the variations in the miRNA profiles of the thyroid gland. A total of 105 miRNAs were differentially expressed in the thyroid gland, most of which were new miRNAs. Through GO and KEGG enrichment analysis, the results showed that the photoperiod response characteristics of Sunite ewes were affected by Olfactory transduction, Wnt signaling pathways, and Apelin signaling pathways. A different illumination time may have a certain influence on the downstream of these pathways, which leads to the change in animal estrus state. In addition, lncRNA-mRNA-miRNA network analysis revealed the target binding sites of identified miRNAs in DE-circRNA and DE-mRNA, such as Novel_369, Novel_370, Novel_461, and so on. The results of this study will provide some new insights into the function of miRNA and the changes in sheep thyroid glands under different photoperiods. Full article

Equine herpesvirus type 1 (EHV-1) causes rhinopneumonitis, abortion, and neurological outbreaks (equine herpesvirus myeloencephalopathy, EHM) in horses. EHV-1 also causes lethal encephalitis in small laboratory animals such as mice and hamsters experimentally. EHV-1 ORF76 is a homolog of HSV-1 US9, which is a herpesvirus kinase. Starting with an EHV-1 bacterial artificial chromosome clone of neuropathogenic strain Ab4p (pAb4p BAC), we constructed an ORF76 deletion mutant (Ab4p∆ORF76) by replacing ORF76 with the rpsLneo gene. Deletion of ORF76 had no influence on replication, cell-to-cell spread in cultured cells, or replication in primary neuronal cells. In Western blots of EHV-1-infected cell lysates, an EHV-1 US9-specific polyclonal antibody detected multiple bands ranging from 35 to 42 kDa. In a CBA/N1 mouse infection model following intranasal inoculation, the parent and Ab4p∆ORF76 revertant caused the same histopathology in the brain and olfactory bulbs. The parent, Ab4p∆ORF76, and revertant mutant replicated similarly in the olfactory mucosa, although Ab4p∆ORF76 was not transported to the olfactory bulbs and was unable to infect the CNS. These results indicated that ORF76 (US9) plays an essential role in the anterograde spread of EHV-1. Full article

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography–mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies. Full article

Domesticated animals are artificially selected to exhibit desirable traits, however not all traits of domesticated animals are the result of deliberate selection. Loss of olfactory capacity in the domesticated pig (Sus scrofa domesticus) is one example. We used whole transcriptome analysis (RNA-Seq) to compare patterns of gene expression in the olfactory mucosa of the pig and two subspecies of wild boar (Sus scrofa), and investigate candidate genes that could be responsible for the loss of olfactory capacity. We identified hundreds of genes with reductions in transcript abundance in pig relative to wild boar as well as differences between the two subspecies of wild boar. These differences were detected mainly in genes involved in the formation and motility of villi, cilia and microtubules, functions associated with olfaction. In addition, differences were found in the abundances of transcripts of genes related to immune defenses, with the highest levels in continental wild boar subspecies. Overall, the loss of olfactory capacity in pigs appears to have been accompanied by reductions in the expression of candidate genes for olfaction. These changes could have resulted from unintentional selection for reduced olfactory capacity, relaxed selection for maintaining olfactory capacity, pleiotropic effects of genes under selection, or other non-selective processes. Our findings could be a cornerstone for future researches on wild boars, pigs, feral populations, and their evolutionary trajectories, aimed to provide tools to better calibrate species management as well as guidelines for breeders. Full article

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field. Full article

Color and odor are crucial cues for butterflies during foraging and courtship. While most sexual dimorphic butterflies rely more on vision, our understanding of how butterflies with similar coloration use different signals remains limited. This study investigated the visual and olfactory behavioral responses of the similarly colored butterfly Byasa hedistus during foraging and courtship. While visiting artificial flowers of different colors, we found that B. hedistus exhibits an innate color preference, with a sequence of preferences for red, purple, and blue. The frequency of flower visits by B. hedistus significantly increased when honey water was sprayed on the artificial flowers, but it hardly visited apetalous branches with honey water. This proves that locating nectar sources by odor alone is difficult in the absence of floral color guides. During courtship, males are active while females hardly chase; only two models were observed: males chasing males and males chasing females. The courtship process includes four behaviors: slowing approach, straight chasing, hovering, and spinning. B. hedistus cannot distinguish between sexes based on color, as there is no significant difference in color and shape between them. Twenty-three VOCs (>1%) were identified in B. hedistus, with 21 shared by both sexes, while ketones are specific to males. These VOCs are principally represented by cineole, β-pinene, and linalool. When cineole was added to butterfly mimics, many butterflies were attracted to them, but the butterflies did not seem to distinguish between males and females. This suggests that cineole may be the feature VOC for identifying conspecific groups. Adding β-pinene and linalool to mimics induced numerous butterflies to chase, hover, spin around, and attempt to mate with them. This suggests that β-pinene and linalool are crucial cues indicating the presence of females. Full article

Rational utilization of soil total nitrogen is one of the keys to achieving sustainable agricultural development. By accurately measuring the content of total nitrogen in the soil, the utilization efficiency of nitrogen in the soil can be improved, and the scientific use of chemical fertilizers can reduce the pressure of agriculture on natural resources and realize the sustainable development of agriculture. In order to measure soil total nitrogen content simply and accurately, combined with the method of artificial olfactory systems, a new method of soil total nitrogen content detection based on convolutional noise reduction autoencoder (CDAE)–whale optimization algorithm (WOA)–deep residual shrinkage network (DSRN) is proposed. In order to obtain more salient features for fusion, the channel mechanism of the DSRN is improved by adding global Max pooling. The model uses a CDAE for the first filtering stage to automatically obtain data that filters simple noise and uses the WOA to automatically optimize hyperparameters. Finally, the optimized hyperparameters were used to train the DRSN for secondary filtering and predict the soil total nitrogen content. Experimental results show that the R² of CAE-WOA-DSRN test set is 0.968, which is significantly better than the R² of a traditional algorithm (0.873) and a simple BP network (0.877), and it can more accurately measure soil total nitrogen content. Full article

Artificial scent screening systems, inspired by the mammalian olfactory system, hold promise for fruit ripeness detection, but their commercialization is limited by low sensitivity or pattern recognition inaccuracy. This study presents a portable fruit ripeness prediction system based on colorimetric sensing combinatorics and deep convolutional neural networks (DCNN) to accurately identify fruit ripeness. Using the gas chromatography–mass spectrometry (GC-MS) method, the study discerned the distinctive gases emitted by mango, peach, and banana across various ripening stages. The colorimetric sensing combinatorics utilized 25 dyes sensitive to fruit volatile gases, generating a distinct scent fingerprint through cross-reactivity to diverse concentrations and varieties of gases. The unique scent fingerprints can be identified using DCNN. After capturing colorimetric sensor image data, the densely connected convolutional network (DenseNet) was employed, achieving an impressive accuracy rate of 97.39% on the validation set and 82.20% on the test set in assessing fruit ripeness. This fruit ripeness prediction system, coupled with a DCNN, successfully addresses the issues of complex pattern recognition and low identification accuracy. Overall, this innovative tool exhibits high accuracy, non-destructiveness, practical applicability, convenience, and low cost, making it worth considering and developing for fruit ripeness detection. Full article

The technological advancements in sensory systems and robotics over the past decade have facilitated the innovation of centralized systems for optimizing resource utilization and monitoring efficiency in inspection applications. This paper presents a novel system designed for gas concentration sensing in environments by implementing a modular artificial nose (emulating the inhalation and exhalation process) equipped with a strategically designed air capture centralization system based on computational fluid dynamics analysis (CFD). The system incorporates three gas identification sensors distributed within the artificial nose, and their information is processed in real-time through embedded systems. The artificial nose is hardware–software integrated with a quadruped robot capable of traversing the environment to collect samples, maximizing coverage area through its mobility and locomotion capabilities. This integration provides a comprehensive perspective on gas distribution in a specific area, enabling the efficient detection of substances in the surrounding environment. The robotic platform employs a graphical interface for real-time gas concentration data map visualization. System integration is achieved using the Robot Operating System (ROS), leveraging its modularity and flexibility advantages. This innovative robotic approach offers a promising solution for enhanced environmental inspection and monitoring applications. Full article

The artificial olfactory image was proposed by Lundström et al. in 1991 as a new strategy for an electronic nose system which generated a two-dimensional mapping to be interpreted as a fingerprint of the detected gas species. The potential distribution generated by the catalytic metals integrated into a semiconductor field-effect structure was read as a photocurrent signal generated by scanning light pulses. The impact of the proposed technology spread beyond gas sensing, inspiring the development of various imaging modalities based on the light addressing of field-effect structures to obtain spatial maps of pH distribution, ions, molecules, and impedance, and these modalities have been applied in both biological and non-biological systems. These light-addressing technologies have been further developed to realize the position control of a faradaic current on the electrode surface for localized electrochemical reactions and amperometric measurements, as well as the actuation of liquids in microfluidic devices. Full article