No abstract

Rapid worldwide decreases in physical activity (PA), an increase in sedentary behaviour (SB) and poorer dietary patterns have been reported during COVID-19 confinement periods. However, as national variability has been observed, this study sought to describe PA, SB and eating patterns, and to explore their gender as well as other socio-demographic correlates and how they interrelate in a representative sample of Portuguese adults during the COVID-19 first mandatory social confinement. The survey was applied online and by telephone to 5856 adults (mean age = 45.8 years; 42.6% women). The majority reported high (46.0%) or moderate (20.5%) PA levels. Men, younger participants, those with higher education levels and a favourable perception of their financial situation reported higher PA levels, with the opposite pattern for SB. Physical fitness activities and household chores were more reported by women, with more strength training and running activities reported by men. Regarding eating behaviours, 45.1% reported changes, positive (58%) and negative (42%), with 18.2% reporting increases in consumption of fruit, vegetables, and fish and other seafood consumption, while 10.8% (most with lower educational level and less comfortable with their income) reported an increase in consumption of ready-to-eat meals, soft drinks, savoury snacks, and take-away and delivered meals. Two clusters—a health-enhancing vs. risky pattern—emerged through multiple correspondence analysis characterized by co-occurrence of high vs. low PA levels, positive vs. negative eating changes, awareness or not of the COVID-19 PA and dietary recommendations, perceived financial situation, higher vs. lower educational level and time in social confinement. In conclusion, while in social confinement, both positive and negative PA and eating behaviours and trends were displayed, highlighting the role of key sociodemographic correlates contributing to healthy vs. risky patterns. Results may inform future health interventions and policies to be more targeted to those at risk, and also advocate the promotion of PA and healthy eating in an integrated fashion.

Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the present work proposes a novel framework based on scientific machine learning to undertake an emerging problem in flavor engineering and industry. It proposes a combining system composed of generative and reinforcement learning models. Therefore, this work brings an innovative methodology to design new flavor molecules. The molecules were evaluated regarding synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product. This work brings as contributions the implementation of a web scraper code to sample a flavors database and the integration of two scientific machine learning techniques in a complex system as a framework. The implementation of the complex system instead of the generative model by itself obtained 10% more molecules within the optimal results. The designed molecules obtained as an output of the reinforcement learning model’s generation were assessed regarding their existence or not in the market and whether they are already used in the flavor industry or not. Thus, we corroborated the potentiality of the framework presented for the search of molecules to be used in the development of flavor-based products.

The aim of present study was two-fold: i) to translate and adapt the Regulation of Eating Behavior Scale to Portuguese (REBSp), and ii) to analyze its psychometrics properties (factorial validity with gender invariance analyses, reliability and construct validity). The study sample was composed by 471 Portuguese participants (68.4% females) with a mean age of 30.5 years (SD = 11.2). Structural equation modeling was used to verify the psychometric properties of the scale using SPSS v. 23.0 and AMOS 24.0 software. The analysis showed that the Portuguese 24-item scale presented a good fit, achieving good reliability and convergent validity. Some issues arose with discriminant validity within autonomous and controlled regulations, discussed in light of the simplex pattern expected by self-determination theory literature. Additionally, the scale presented concurrent validity and evidence of gender measurement invariance. Latent mean analysis between genders showed that women presented higher means for intrinsic motivation and integrated regulation when compared to men. In conclusion, analyses suggest that the 24-item Portuguese version of REBS can be used safely to assess the eating regulation in both genders.

The flavor is an essential component in developing numerous products in the market. The increasing consumption of processed and fast food and healthy packages has upraised the investment in new flavoring agents and, consequently, molecules with flavoring properties. In this context, this work brings a Scientific Machine Learning approach to address this product engineering need. Scientific Machine Learning in computational chemistry has opened paths in predicting a compound's properties without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules.