The aminoaldehydes 4-aminobutanal and 5-aminopentanal, derived from the oxidation of the diamines putrescine and cadaverine, and 1-(3-aminopropyl)-4-aminobutanal and aminodialdehyde, derived from the oxidation of the polyamines spermidine and spermine, were produced utilizing a copper amine oxidase (CAO) from Euphorbia characias latex and tested with in vitro cultivation of Leishmania infantum promastigotes. Whereas the aminoaldehydes derived from the oxidation of the diamines were stimulating factors for growth of Leishmania infantum promastigotes, the aldehydes derived from polyamines oxidation had a drastic inhibitory effect on the vitality and growth of these parasites. Thus, a double scenario arises, showing the use of aldehydes from diamines to obtain a large number of organisms of Leishmania infantum promastigotes to use in serological studies, whereas the aldehydes derived from polyamines could be used as a new strategy for therapeutic treatment against these parasites.

The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.

The 2018 Global Nutrition Report. 1 of the World Health Organization (WHO) reveals that malnutrition affects, in different forms, every country of the world. Malnutrition determines more health issues than any other cause, and progress towards better nutrition is still too slow. In particular, in developed countries, overweight and obesity in adults are a cause of several non-communicable diseases, including diabetes, heart disease, stroke, different types of cancer, musculoskeletal disorders, and respiratory symptoms. 2 Micronutrient deficiencies are also cause of severe health issues, such as anaemia.

Recently, high-density (HD) EMG electrodes have been proposed for improving amputees' movement/grasping intention recognition, exploiting different machine learning techniques. HD EMG electrodes are composed of a large number of closely spaced channels that simultaneously acquire EMG signals from different parts of the muscle. Given the topological properties of these devices, it is important to fully exploit the spatiotemporal information provided by the electrodes to optimize recognition accuracy. In this work, we introduce the use of Graph Neural Networks (GNNs) to process HD EMG data for movement intention recognition of people with an amputation affecting the upper limbs and which use a robotic prosthesis. In this initial investigation of the approach, we conducted experiments using a real-world dataset consisting of EMG signals collected from 20 volunteers while performing 65 different gestures. We were able to detect 45 gestures with a classification error rate of less than 10%, and obtained an overall classification error rate of 8.75% with a standard deviation of 4.9. To the best of our knowledge, this is the first work in which GNNs are used for processing HD EMG data.

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