Dataset Information

Optimizing Drosophila olfactory learning with a semi-automated training device.

ABSTRACT: Drosophila olfactory aversive conditioning has served as a powerful model system with which to elucidate the molecular and neuronal mechanisms underlying memory formation. In the typical protocol, flies are exposed to a constant odor stream while receiving a pulsed electric shock in the conditioning tube of a manual apparatus. We have devised a simple, low-cost semi-automated conditioning apparatus that computationally controls the delivery of odor and shock. A semiconductor-based odor sensor is employed to monitor the change of odor concentration in the training tube. The system thus allows electric shocks to be precisely matched with odor concentration in the training tube. We found that short-term memory performance was improved with a pulsed odor flow protocol, in which odor is presented in short pulses, each paired with electric shock, rather than as a constant flow. The effect of pulsed odor flow might be ascribed to the phenomenon of 'conditioned approach', where approach toward an odor is induced when the electric shock is presented before odor pulse ends. Our data shows that the system is applicable to the study of olfactory memory formation and to the examination of conditioning parameters at a level of detail not practical with a manual apparatus.

SUBMITTER: Murakami S

PROVIDER: S-EPMC2854214 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Optimizing Drosophila olfactory learning with a semi-automated training device.

Murakami Satoshi S Dan Chuntao C Zagaeski Brendan B Maeyama Yuko Y Kunes Sam S Tabata Tetsuya T

Journal of neuroscience methods 20100212 2

Drosophila olfactory aversive conditioning has served as a powerful model system with which to elucidate the molecular and neuronal mechanisms underlying memory formation. In the typical protocol, flies are exposed to a constant odor stream while receiving a pulsed electric shock in the conditioning tube of a manual apparatus. We have devised a simple, low-cost semi-automated conditioning apparatus that computationally controls the delivery of odor and shock. A semiconductor-based odor sensor is ...[more]

PMID: 20153774

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Project description:Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system’s economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians.

Dataset Information

Optimizing Drosophila olfactory learning with a semi-automated training device.

Publications

Optimizing Drosophila olfactory learning with a semi-automated training device.

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