Exploring ultrasonic communication in mice treated with Cannabis sativa oil: Audio data processing and correlation study with different behaviours.

Studying ultrasonic vocalizations (USVs) plays a crucial role in understanding animal communication, particularly in the field of ethology and neuropharmacology. Communication is associated with social behaviour; so, USVs study is a valid assay in behavioural readout and monitoring in this context. This paper delved into an investigation of ultrasonic communication in mice treated with Cannabis sativa oil (CS mice), which has been demonstrated having a prosocial effect on behaviour of mice, versus control mice (vehicle-treated, VH mice). To conduct this study, we created a dataset by recording audio-video files and annotating the duration of time that test mice spent engaging in social activities, along with categorizing the types of emitted USVs. The analysis encompassed the frequency of individual sounds as well as more complex sequences of consecutive syllables (patterns). The primary goal was to examine the extent and nature of diversity in ultrasonic communication patterns emitted by these two groups of mice. As a result, we observed statistically significant differences for each considered pattern length between the two groups of mice. Additionally, the study extended its research by considering specific behaviours, aiming to ascertain whether dissimilarities in ultrasonic communication between CS and VH mice are more pronounced or subtle within distinct behavioural contexts. Our findings suggest that while there is variation in USV communication between the two groups of mice, the degree of this diversity may vary depending on the specific behaviour being observed.

Extended performance analysis of deep-learning algorithms for mice vocalization segmentation.

Ultrasonic vocalizations (USVs) analysis represents a fundamental tool to study animal communication. It can be used to perform a behavioral investigation of mice for ethological studies and in the field of neuroscience and neuropharmacology. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and then processed by specific software, which help the operator to identify and characterize different families of calls. Recently, many automated systems have been proposed for automatically performing both the detection and the classification of the USVs. Of course, the USV segmentation represents the crucial step for the general framework, since the quality of the call processing strictly depends on how accurately the call itself has been previously detected. In this paper, we investigate the performance of three supervised deep learning methods for automated USV segmentation: an Auto-Encoder Neural Network (AE), a U-NET Neural Network (UNET) and a Recurrent Neural Network (RNN). The proposed models receive as input the spectrogram associated with the recorded audio track and return as output the regions in which the USV calls have been detected. To evaluate the performance of the models, we have built a dataset by recording several audio tracks and manually segmenting the corresponding USV spectrograms generated with the Avisoft software, producing in this way the ground-truth (GT) used for training. All three proposed architectures demonstrated precision and recall scores exceeding [Formula: see text], with UNET and AE achieving values above [Formula: see text], surpassing other state-of-the-art methods that were considered for comparison in this study. Additionally, the evaluation was extended to an external dataset, where UNET once again exhibited the highest performance. We suggest that our experimental results may represent a valuable benchmark for future works.

Combining Appearance and Gradient Information for Image Symmetry Detection.

This work addresses the challenging problem of reflection symmetry detection in unconstrained environments. Starting from the understanding on how the visual cortex manages planar symmetry detection, it is proposed to treat the problem in two stages: i) the design of a stable metric that extracts subsets of consistently oriented candidate segments, whenever the underlying 2D signal appearance exhibits definite near symmetric correspondences; ii) the ranking of such segments on the basis of the surrounding gradient orientation specularity, in order to reflect real symmetric object boundaries. Since these operations are related to the way the human brain performs planar symmetry detection, a better correspondence can be established between the outcomes of the proposed algorithm and a human-constructed ground truth. When compared to the testing sets used in recent symmetry detection competitions, a remarkable performance gain can be observed. In additional, further validation has been achieved by conducting perceptual validation experiments with users on a newly built dataset.

Automatic classification of mice vocalizations using Machine Learning techniques and Convolutional Neural Networks.

Ultrasonic vocalizations (USVs) analysis is a well-recognized tool to investigate animal communication. It can be used for behavioral phenotyping of murine models of different disorders. The USVs are usually recorded with a microphone sensitive to ultrasound frequencies and they are analyzed by specific software. Different calls typologies exist, and each ultrasonic call can be manually classified, but the qualitative analysis is highly time-consuming. Considering this framework, in this work we proposed and evaluated a set of supervised learning methods for automatic USVs classification. This could represent a sustainable procedure to deeply analyze the ultrasonic communication, other than a standardized analysis. We used manually built datasets obtained by segmenting the USVs audio tracks analyzed with the Avisoft software, and then by labelling each of them into 10 representative classes. For the automatic classification task, we designed a Convolutional Neural Network that was trained receiving as input the spectrogram images associated to the segmented audio files. In addition, we also tested some other supervised learning algorithms, such as Support Vector Machine, Random Forest and Multilayer Perceptrons, exploiting informative numerical features extracted from the spectrograms. The performance showed how considering the whole time/frequency information of the spectrogram leads to significantly higher performance than considering a subset of numerical features. In the authors' opinion, the experimental results may represent a valuable benchmark for future work in this research field.

A tool for the quantification of radial neo-vessels in chick chorioallantoic membrane angiogenic assays.

Angiogenesis, the process of new blood vessels formation, plays a key role in different physiological and pathological conditions and it is considered a promising target for the development of new anti-inflammatory and anti-tumor therapies. Several assays have been developed to mimic the angiogenic process in vitro and in vivo. Here we propose a technique for the quantification of the pro-angiogenic or anti-angiogenic responses induced by different molecules when implanted in vivo on the chick embryo chorioallantoic membrane (CAM). At day 11 of development CAM is completely vascularized and neo-vessels induced by exogenous molecules converge radially to the implant. Our algorithm is an effective and rapid tool to characterize molecules endowed with proor anti-angiogenic effects by means of the quantification of the vessels present in the CAM macroscopic images. Based on conventional and dedicated image morphology tools, the proposed technique is able to discriminate radial from non-radial vessels, excluding the last ones from the count.