Transfer entropy ($TE$) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate $TE$, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased $TE$ during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine–SVM, and convolutional neural network–CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p< 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

Recently, the goal of space exploration has shifted from the incognito of the solar system to the Moon. Concepts like human permanence on the Moon and thermal protective structures made with ISRU (in situ resource utilization) of raw materials have started to be implemented. By limiting the need to launch supplies from the Earth, the paradigm of spaceflight is changed, privileging the vanguard of the utilisation of resources in situ. Still, the main challenges of surviving the radiation dose and the cryogenic temperatures of the lunar night remain. Recent studies have demonstrated how innovative composite materials can help reduce the temperature stress on exploration vehicles. This research presents the material properties of aerogel insulating materials combined with LHS (lunar highlands simulant) regolith obtained by freeze frying. Organic-based aerogels with different percentages of LHS have been analysed in terms of material, morphology, and thermal properties.

Objective: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. Methods: This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. Results: Results showed a satisfactory performance in terms of low MAE ( $0.2370 \pm 0.0086$ ). However, the unbalanced distribution of colors in the data affects this performance. Significance: This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.

Objective. The study of neurovascular coupling (NVC), the relationship between neuronal activity and cerebral blood flow, is essential for understanding brain physiology in both healthy and pathological states. Current methods to study NVC include neuroimaging techniques with limited temporal resolution and indirect neuronal activity measures. Methods including electroencephalographic (EEG) data are predominantly linear and display limitations that nonlinear methods address. Transfer entropy (TE) explores linear and nonlinear relationships simultaneously. This study hypothesizes that complex NVC interactions in stroke patients, both linear and nonlinear, can be detected using TE. Approach. TE between simultaneously recorded EEG and cerebral blood flow velocity (CBFV) signals was computed and analyzed in three settings: ipsilateral (EEG and CBFV from same hemisphere) stroke and nonstroke, and contralateral (EEG from stroke hemisphere, CBFV from nonstroke hemisphere). A surrogate analysis was performed to evaluate the significance of TE values and to identify the nature of the interactions. Main results. The results showed that EEG generally influenced CBFV. There were more linear+nonlinear interactions in the ipsilateral nonstroke setting and in the delta band in ipsilateral stroke and contralateral settings. Interactions between EEG and CBFV were stronger on the nonstroke side for linear+nonlinear dynamics. The strength and nature of the interactions were weakly correlated with clinical outcomes (e.g. delta band ( p < 0.05): infarct growth linear = −0.448, linear+nonlinear = −0.339; NIHSS linear = −0.473, linear+nonlinear = −0.457). Significance. This study exemplifies the benefits of using TE in linear and nonlinear NVC analysis to better understand the implications of these dynamics in stroke severity.