Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.

This paper proposes a protocol for the acquisition and processing of biophysical signals in virtual reality applications, particularly in phobia therapy experiments. This protocol aims to ensure that the measurement and processing phases are performed effectively, to obtain clean data that can be used to estimate the users’ anxiety levels. The protocol has been designed after analyzing the experimental data of seven subjects who have been exposed to heights in a virtual reality environment. The subjects’ level of anxiety has been estimated based on the real-time evaluation of a nonlinear function that has as parameters various features extracted from the biophysical signals. The highest classification accuracy was obtained using a combination of seven heart rate and electrodermal activity features in the time domain and frequency domain.

The vermiform appendix is a muscular cylindrical structure originating near the junction of the cecum and ileum, averaging 9 cm (5–35 cm) in size. As the most mobile viscera, it can adopt several positions, the most common being the retrocecal position. Perceived as an atavistic organ lacking physiological relevance, the vermiform appendix appears to be involved in immune function, serving in the maturation of B lymphocytes and the production of immunoglobulin A, in endocrine function, excreting amines and hormones in the 2–3 mL of mucus secreted daily, and in digestive function, by storing beneficial bacteria from where they can recolonize the colon. With a lumen of about 6 mm, the vermiform appendix has a reduced storage capacity, so any blockage of the appendix with fecoliths (fecaliths), seeds derailed from the colon, or enlarged lymph nodes prevents drainage and intraluminal accumulation of secreted mucus. Unable to relax, the appendix wall severely limits its intraluminal volume, so mucus accumulation leads to inflammation of the appendix, known generically as appendicitis. In addition, the vermiform appendix may be the site of the development of neoplastic processes, which may or may not involve mucus production, some of which can significantly affect the standard of living and ultimately lead to death. In general, mucinous tumors may have a better prognosis than non-mucinous tumors. This review takes a comprehensive path, starting by describing the anatomy and embryology of the vermiform appendix and further detailing its inflammatory pathologies, pathologies related to congenital anomalies, and appendix tumors, thus creating an up-to-date framework for better understanding, diagnosis, and treatment of these health problems.

Due to their outstanding properties, quantum dots (QDs) received a growing interest in the biomedical field, but it is of major importance to investigate and to understand their interaction with the biomolecules. We examined the stability of silicon QDs and the time evolution of QDs – protein corona formation in various biological media (bovine serum albumin, cell culture medium without or supplemented with 10% fetal bovine serum-FBS). Changes in the secondary structure of BSA were also investigated over time. Hydrodynamic size and zeta potential measurements showed an evolution in time indicating the nanoparticle-protein interaction. The protein corona formation was also dependent on time, albumin adsorption reaching the peak level after 1 hour. The silicon QDs adsorbed an important amount of FBS proteins from the first 5 minutes of incubation that was maintained for the next 8 hours, and diminished afterwards. Under protein-free conditions the QDs induced cell membrane damage in a time-dependent manner, however the presence of serum proteins attenuated their hemolytic activity and maintained the integrity of phosphatidylcholine layer. This study provides useful insights regarding the dynamics of BSA adsorption and interaction of silicon QDs with proteins and lipids, in order to understand the role of QDs biocorona.

This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants’ ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms—Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks—accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms’ classification scores.

Since the discovery of lanthanides, the expanding range of applications and the growing demand for lanthanides in different aspects of life have escalated their dispersion in the environment, raising concerns about their impact on the living world. This review explores the interaction between lanthanides and different groups of living organisms (bacteria, algae, lichens, plants, invertebrates, and low vertebrates), reflecting the current state of scientific knowledge. We have aimed to provide a comprehensive overview of relevant studies, highlight existing gaps, and suggest potential areas for future research to enhance the understanding of this topic.