Evidence of learning walks related to scorpion home burrow navigation

The Navigation by Chemotextural Familiarity Hypothesis (NCFH) suggests that scorpions use their midventral pectines to gather chemical and textural information near their burrows and use this information as they subsequently return home. For NCFH to be viable, animals must somehow acquire home-directed ″tastes″ of the substrate, such as through path integration (PI) and/or learning walks. We conducted laboratory behavioral trials using desert grassland scorpions (Paruroctonus utahensis). Animals reliably formed burrows in small mounds of sand we provided in the middle of circular, sand lined behavioral arenas. We processed overnight infrared video recordings with a MATLAB script that tracked animal movements at 1-2 s intervals. In all, we analyzed the movements of 23 animals, representing nearly 1500 hours of video recording. We found that once animals established their home burrows, they immediately made one to several short, looping excursions away from and back to their burrows before walking greater distances. We also observed similar excursions when animals made burrows in level sand in the middle of the arena (i.e., no mound provided). These putative learning walks, together with recently reported PI in scorpions, may provide the crucial home-directed information requisite for NCFH.

Optical-flow-based motion compensation algorithm in thermoelastic stress analysis using single-infrared video

<p>Thermoelastic stress analysis (TSA) is a non-contact measurement technique for stress distribution evaluation. A common issue related to this technique is the rigid-displacement of the specimen during the test phase, that can compromise the reliability of the measurement. For this purpose, several motion compensation techniques have been implemented over the years, but none of them is provided through a single measurement and a single sample surface conditioning. Due to this, a motion compensation technique based on Optical-Flow has been implemented, which greatly increases the strength and the effectiveness of the methodology through a single measurement and single specimen preparation. The proposed approach is based on measuring the displacement field of the specimen directly from the thermal video, through optical flow. This displacement field is then used to compensate for the specimen’s displacement on the infrared video, which will then be used for thermoelastic stress analysis. Firstly, the algorithm was validated by a comparison with synthetic videos, created ad hoc, and the quality of the motion compensation approach was evaluated on video acquired in the visible range. The research moved into infrared acquisitions, where the application of TSA gave reliable and accurate results. Finally, the quality of the stress map obtained was verified by comparison with a numerical model.</p>

Noncontact Sleep Monitoring With Infrared Video Data to Estimate Sleep Apnea Severity and Distinguish Between Positional and Nonpositional Sleep Apnea: Model Development and Experimental Validation

Background Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy (eg, wearing an item to encourage sleeping toward the lateral position) to treat positional apnea. The gold standard of diagnosing sleep apnea and whether or not it is positional is polysomnography; however, this test is inconvenient, expensive, and has a long waiting list. Objective The objective of this study was to develop and evaluate a noncontact method to estimate sleep apnea severity and to distinguish positional versus nonpositional sleep apnea. Methods A noncontact deep-learning algorithm was developed to analyze infrared video of sleep for estimating AHI and to distinguish patients with positional vs nonpositional sleep apnea. Specifically, a 3D convolutional neural network (CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app.

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos.

Qualitative and quantitative analyses of polysomnographic measurements in foals

Veterinary and human medicine are still seeking a conclusive explanation of the function of sleep, including the change in sleep behaviour over the course of an individual’s lifetime. In human medicine, sleep disorders and abnormalities in the electroencephalogram are used for prognostic statements, therapeutic means and diagnoses. To facilitate such use in foal medicine, we monitored 10 foals polysomnographically for 48 h. Via 10 attached cup electrodes, brain waves were recorded by electroencephalography, eye movements by electrooculography and muscle activity by electromyography. Wireless polysomnographs allowed us to measure the foals in their home stables. In addition, each foal was simultaneously monitored with infrared video cameras. By combining the recorded data, we determined the time budgeting of the foals over 48 h, whereby the states of vigilance were divided into wakefulness, light sleep, slow-wave sleep and rapid-eye-movement sleep, and the body positions into standing, suckling, sternal recumbency and lateral recumbency. The results of the qualitative analyses showed that the brain waves of the foals differ in their morphology from those previously reported for adult horses. The quantitative data analyses revealed that foals suckle throughout all periods of the day, including night-time. The results of our combined measurements allow optimizing the daily schedule of the foals according to their sleep and activity times. We recommend that stall rest should begin no later than 9.00 p.m. and daily stable work should be done in the late afternoon.