The interpretability of classification systems refers to the ability of these to express their behaviour in a way that is easily understandable by a user. Interpretable classification models allow for external validation by an expert and, in certain disciplines such as medicine or business, providing information about decision making is essential for ethical and human reasons. Fuzzy rule-based classification systems are consolidated powerful classification tools based on fuzzy logic and designed to produce interpretable models; however, in presence of a large number of attributes, even rule-based models tend to be too complex to be easily interpreted. In this work, we propose a novel multivariate feature selection method in which both search strategy and classifier are based on multi-objective evolutionary computation. We designed a set of experiments to establish an acceptable setting with respect to the number of evaluations required by the search strategy and by the classifier, and we tested our strategy on a real-life dataset. Then, we compared our results against a wide range of feature selection methods that includes filter, wrapper, multivariate and univariate methods, with deterministic and probabilistic search strategies, and with evaluators of diverse nature. Finally, the fuzzy rule based classification model obtained with the proposed method has been evaluated with standard performance metrics and compared with other well-known fuzzy rule-based classifiers. We have used two real-life datasets extracted from a contact center; in one case, with the proposed method we obtained an accuracy of 0.7857 with 8 rules, while the best fuzzy classifier compared obtained 0.7679 with 8 rules, and in the second case, we obtained an accuracy of 0.7403 with 5 rules, while the best fuzzy classifier compared obtained 0.6364 with 4 rules.

Molecular Dynamics (MD) simulations are essential in analyzing the physical movement of molecules, with GROMACS being a widely recognized open-source package for this purpose. However, conducting analyses individually in GROMACS can take excessive time and effort. Addressing this challenge, we introduce ASGARD, an innovative workflow designed to streamline and automate the analysis of MD simulation of protein or protein-ligand complex. Unlike the traditional, manual approach, ASGARD enables researchers to generate comprehensive analyses with a single command line, significantly accelerating the research process and avoiding the laborious task of manual report generation. This tool automatically performs a range of analyses post-simulation, including system stability and flexibility assessments through RMSD Fluctuation and Distribution calculations. It further provides dynamic analysis using SASA, DSSP method graphs, and various interaction analyses. A key feature of ASGARD is its user-friendly design; it requires no additional installations or dependencies, making it highly accessible for researchers. In conclusion, ASGARD simplifies the MD simulation analysis process and substantially enhances efficiency and productivity in molecular research by providing an integrated, one-command analysis solution.Communicated by Ramaswamy H. Sarma.

The endogenous protease furin is a key protein in many different diseases, such as cancer and infections. For this reason, a wide range of studies has focused on targeting furin from a therapeutic point of view. Our main objective consisted of identifying new compounds that could enlarge the furin inhibitor arsenal; secondarily, we assayed their adjuvant effect in combination with a known furin inhibitor, CMK, which avoids the SARS-CoV-2 S protein cleavage by means of that inhibition. Virtual screening was carried out to identify potential furin inhibitors. The inhibition of physiological and purified recombinant furin by screening selected compounds, Clexane, and these drugs in combination with CMK was assayed in fluorogenic tests by using a specific furin substrate. The effects of the selected inhibitors from virtual screening on cell viability (293T HEK cell line) were assayed by means of flow cytometry. Through virtual screening, Zeaxanthin and Kukoamine A were selected as the main potential furin inhibitors. In fluorogenic assays, these two compounds and Clexane inhibited both physiological and recombinant furin in a dose-dependent way. In addition, these compounds increased physiological furin inhibition by CMK, showing an adjuvant effect. In conclusion, we identified Kukoamine A, Zeaxanthin, and Clexane as new furin inhibitors. In addition, these drugs were able to increase furin inhibition by CMK, so they could also increase its efficiency when avoiding S protein proteolysis, which is essential for SARS-CoV-2 cell infection.

Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair (p = 0.033), empathy (p = 0.030), respect (p = 0.038), and global communication skills (p = 0.036). Overall, sex and emotional intelligence dimensions are important to detect this trait, although personal characteristics should be considered. The present individualized prediction model could help to predict the presence of the SPS trait in nursing students, which may be useful in conducting intervention strategies to avoid the negative consequences and reinforce the positive ones of this trait.