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Learning & Memory (Cold Spring Harbor,... May 2024Associative learning enables the adaptive adjustment of behavioral decisions based on acquired, predicted outcomes. The valence of what is learned is influenced not only...
Associative learning enables the adaptive adjustment of behavioral decisions based on acquired, predicted outcomes. The valence of what is learned is influenced not only by the learned stimuli and their temporal relations, but also by prior experiences and internal states. In this study, we used the fruit fly to demonstrate that neuronal circuits involved in associative olfactory learning undergo restructuring during extended periods of low-caloric food intake. Specifically, we observed a decrease in the connections between specific dopaminergic neurons (DANs) and Kenyon cells at distinct compartments of the mushroom body. This structural synaptic plasticity was contingent upon the presence of allatostatin A receptors in specific DANs and could be mimicked optogenetically by expressing a light-activated adenylate cyclase in exactly these DANs. Importantly, we found that this rearrangement in synaptic connections influenced aversive, punishment-induced olfactory learning but did not impact appetitive, reward-based learning. Whether induced by prolonged low-caloric conditions or optogenetic manipulation of cAMP levels, this synaptic rearrangement resulted in a reduction of aversive associative learning. Consequently, the balance between positive and negative reinforcing signals shifted, diminishing the ability to learn to avoid odor cues signaling negative outcomes. These results exemplify how a neuronal circuit required for learning and memory undergoes structural plasticity dependent on prior experiences of the nutritional value of food.
Topics: Animals; Mushroom Bodies; Drosophila melanogaster; Neuronal Plasticity; Dopaminergic Neurons; Eating; Optogenetics; Association Learning; Smell; Olfactory Perception; Reward; Animals, Genetically Modified
PubMed: 38862177
DOI: 10.1101/lm.053997.124 -
Learning & Memory (Cold Spring Harbor,... May 2024In this review, we aggregated the different types of learning and memory paradigms developed in adult and attempted to assess the similarities and differences in the... (Review)
Review
In this review, we aggregated the different types of learning and memory paradigms developed in adult and attempted to assess the similarities and differences in the neural mechanisms supporting diverse types of memory. The simplest association memory assays are conditioning paradigms (olfactory, visual, and gustatory). A great deal of work has been done on these memories, revealing hundreds of genes and neural circuits supporting this memory. Variations of conditioning assays (reversal learning, trace conditioning, latent inhibition, and extinction) also reveal interesting memory mechanisms, whereas mechanisms supporting spatial memory (thermal maze, orientation memory, and heat box) and the conditioned suppression of innate behaviors (phototaxis, negative geotaxis, anemotaxis, and locomotion) remain largely unexplored. In recent years, there has been an increased interest in multisensory and multicomponent memories (context-dependent and cross-modal memory) and higher-order memory (sensory preconditioning and second-order conditioning). Some of this work has revealed how the intricate mushroom body (MB) neural circuitry can support more complex memories. Finally, the most complex memories are arguably those involving social memory: courtship conditioning and social learning (mate-copying and egg-laying behaviors). Currently, very little is known about the mechanisms supporting social memories. Overall, the MBs are important for association memories of multiple sensory modalities and multisensory integration, whereas the central complex is important for place, orientation, and navigation memories. Interestingly, several different types of memory appear to use similar or variants of the olfactory conditioning neural circuitry, which are repurposed in different ways.
Topics: Animals; Memory; Drosophila; Mushroom Bodies; Behavior, Animal
PubMed: 38862165
DOI: 10.1101/lm.053810.123 -
PeerJ. Computer Science 2024The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells,...
The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.
PubMed: 38855210
DOI: 10.7717/peerj-cs.2028 -
PeerJ. Computer Science 2024Numerous impediments beset contemporary art education, notably the unidimensional delivery of content and the absence of real-time interaction during instructional...
Numerous impediments beset contemporary art education, notably the unidimensional delivery of content and the absence of real-time interaction during instructional sessions. This study endeavors to surmount these challenges by devising a multimodal perception system entrenched in Internet of Things (IoT) technology. This system captures students' visual imagery, vocalizations, spatial orientation, movements, ambient luminosity, and contextual data by harnessing an array of interaction modalities encompassing visual, auditory, tactile, and olfactory sensors. The synthesis of this manifold information about learning scenarios entails strategically placing sensors within physical environments to facilitate intuitive and seamless interactions. Utilizing digital art flower cultivation as a quintessential illustration, this investigation formulates tasks imbued with multisensory channel interactions, pushing the boundaries of technological advancement. It pioneers advancements in critical domains such as visual feature extraction by utilizing DenseNet networks and voice feature extraction leveraging SoundNet convolutional neural networks. This innovative paradigm establishes a novel art pedagogical framework, accentuating the importance of visual stimuli while enlisting other senses as complementary contributors. Subsequent evaluation of the usability of the multimodal perceptual interaction system reveals a remarkable task recognition accuracy of 96.15% through the amalgamation of Mel-frequency cepstral coefficients (MFCC) speech features with a long-short-term memory (LSTM) classifier model, accompanied by an average response time of merely 6.453 seconds-significantly outperforming comparable models. The system notably enhances experiential fidelity, realism, interactivity, and content depth, ameliorating the limitations inherent in solitary sensory interactions. This augmentation markedly elevates the caliber of art pedagogy and augments learning efficacy, thereby effectuating an optimization of art education.
PubMed: 38855203
DOI: 10.7717/peerj-cs.2047 -
Social Cognitive and Affective... May 2024The smell of the own baby is a salient cue for human kin recognition and bonding. We hypothesized that infant body odors function like other cues of the Kindchenschema...
The smell of the own baby is a salient cue for human kin recognition and bonding. We hypothesized that infant body odors function like other cues of the Kindchenschema by recruiting neural circuits of pleasure and reward. In two functional magnetic resonance imaging studies, we presented infantile and post-pubertal body odors to nulliparae and mothers (N = 78). All body odors increased blood-oxygen-level-dependent (BOLD) response and functional connectivity in circuits related to olfactory perception, pleasure and reward. Neural activation strength in pleasure and reward areas positively correlated with perceptual ratings across all participants. Compared to body odor of post-pubertal children, infant body odors specifically enhanced BOLD signal and functional connectivity in reward and pleasure circuits, suggesting that infantile body odors prime the brain for prosocial interaction. This supports the idea that infant body odors are part of the Kindchenschema. The additional observation of functional connectivity being related to maternal and kin state speaks for experience-dependent priming.
Topics: Humans; Odorants; Female; Magnetic Resonance Imaging; Male; Infant; Adult; Smell; Brain; Olfactory Perception; Brain Mapping; Oxygen; Young Adult; Child; Reward; Pleasure
PubMed: 38850226
DOI: 10.1093/scan/nsae038 -
Nature Communications Jun 2024Brain evolution has primarily been studied at the macroscopic level by comparing the relative size of homologous brain centers between species. How neuronal circuits...
Brain evolution has primarily been studied at the macroscopic level by comparing the relative size of homologous brain centers between species. How neuronal circuits change at the cellular level over evolutionary time remains largely unanswered. Here, using a phylogenetically informed framework, we compare the olfactory circuits of three closely related Drosophila species that differ in their chemical ecology: the generalists Drosophila melanogaster and Drosophila simulans and Drosophila sechellia that specializes on ripe noni fruit. We examine a central part of the olfactory circuit that, to our knowledge, has not been investigated in these species-the connections between projection neurons and the Kenyon cells of the mushroom body-and identify species-specific connectivity patterns. We found that neurons encoding food odors connect more frequently with Kenyon cells, giving rise to species-specific biases in connectivity. These species-specific connectivity differences reflect two distinct neuronal phenotypes: in the number of projection neurons or in the number of presynaptic boutons formed by individual projection neurons. Finally, behavioral analyses suggest that such increased connectivity enhances learning performance in an associative task. Our study shows how fine-grained aspects of connectivity architecture in an associative brain center can change during evolution to reflect the chemical ecology of a species.
Topics: Animals; Mushroom Bodies; Drosophila; Biological Evolution; Species Specificity; Neurons; Drosophila melanogaster; Phylogeny; Smell; Odorants; Olfactory Pathways; Male; Female; Presynaptic Terminals
PubMed: 38849331
DOI: 10.1038/s41467-024-48839-4 -
Neurobiology of Learning and Memory Jun 2024We have developed a behavioral paradigm to study volitional olfactory investigation in mice over several months. We placed odor ports in the wall of a standard cage that...
We have developed a behavioral paradigm to study volitional olfactory investigation in mice over several months. We placed odor ports in the wall of a standard cage that administer a neutral odorant stimulus when a mouse pokes its nose inside. Even though animals were fed and watered ad libitum, and sampling from the port elicited no outcome other than the delivery of an odor, mice readily sampled these stimuli hundreds of times per day. This self-paced olfactory investigation persisted for weeks with only modest habituation following the first day of exposure to a given set of odorants. If an unexpected odorant stimulus was administered at the port, the sampling rate increased transiently (in the first 20 min) by an order of magnitude and remained higher than baseline throughout the subsequent day, indicating learned implicit knowledge. Thus, this system may be used to study naturalistic olfactory learning over extended time scales outside of conventional task structures.
PubMed: 38844099
DOI: 10.1016/j.nlm.2024.107951 -
Frontiers in Neural Circuits 2024The olfactory tubercle (OT) is a unique part of the olfactory cortex of the mammal brain in that it is also a component of the ventral striatum. It is crucially involved... (Review)
Review
The olfactory tubercle (OT) is a unique part of the olfactory cortex of the mammal brain in that it is also a component of the ventral striatum. It is crucially involved in motivational behaviors, particularly in adaptive olfactory learning. This review introduces the basic properties of the OT, its synaptic connectivity with other brain areas, and the plasticity of the connectivity associated with learning behavior. The adaptive properties of olfactory behavior are discussed further based on the characteristics of OT neuronal circuits.
Topics: Animals; Neuronal Plasticity; Humans; Olfactory Tubercle; Learning
PubMed: 38841557
DOI: 10.3389/fncir.2024.1423505 -
BMC Genomics Jun 2024Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational...
BACKGROUND
Advances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis.
RESULTS
Extensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity.
CONCLUSIONS
HyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.
Topics: Humans; Animals; Mice; Gene Expression Profiling; Transcriptome; Deep Learning; Cluster Analysis; Computational Biology; Breast Neoplasms; Olfactory Bulb
PubMed: 38840049
DOI: 10.1186/s12864-024-10469-x -
Bioinformatics Advances 2024The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of...
MOTIVATION
The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge.
RESULTS
We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues.
AVAILABILITY AND IMPLEMENTATION
Source code and the R software tool STew are available from github.com/fanzhanglab/STew.
PubMed: 38827413
DOI: 10.1093/bioadv/vbae064