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Celem badań było znalezienie związku pomiędzy całkowitą zawartością fenoli i flawonoidów w miodzie oraz jego zdolnością dezaktywacji rodnika DPPH a jego pochodzeniem botanicznym. W badaniach uwzględniono cztery odmiany miodu (kasztanowy, eukaliptusowy, wrzosowy i jeżynowy) oraz miód wielokwiatowy. W miodach tych stwierdzono obecność pyłku pochodzącego z 96 gatunków roślin należących do 50 rodzin. Nie mniej jednak dla produkcji miodu na tym terenie znaczenie miało jedynie kilka gatunków roślin. W badanych miodach w największych ilościach występował pyłek: Castanea sativa, Rubus, Eucalyptus oraz gatunków rodzaju Erica. Inne rodzaje pyłku miały niewielkie znaczenie: Echium, Brassica, Trifolium, Campanulla, Crateagus monogyna, Salix, Frangula alnus, Conium maculatum.

Non-pharmacological interventions (e.g., stay-at-home orders, school closures, physical distancing) implemented during the COVID-19 pandemic are expected to have modified routines and lifestyles, eventually impacting key exposome parameters, including, among others, physical activity, diet and cleaning habits. The objectives were to describe the exposomic profile of the general Cypriot population and compliance to the population-wide measures implemented during March-May 2020 to lower the risk of SARS-CoV-2 transmission, and to simulate the population-wide measures' effect on social contacts and SARS-CoV-2 spread. A survey was conducted in March-May 2020 capturing different exposome parameters, e.g., individual characteristics, lifestyle/habits, time spent and contacts at home/work/elsewhere. We described the exposome parameters and their correlations. In an exposome-wide association analysis, we used the number of hours spent at home as an indicator of compliance to the measures. We generated synthetic human proximity networks, before and during the measures using the dynamic-[Formula: see text] 1 model and simulated SARS-CoV-2 transmission (i.e., to identify possible places where higher transmission/number of cases could originate from) on the networks with a dynamic Susceptible-Exposed-Infectious-Recovered model. Overall, 594 respondents were included in the analysis (mean age 45.7 years, > 50% in very good health and communicating daily with friends/family via phone/online). The median number of contacts at home and at work decreased during the measures (from 3 to 2 and from 12 to 0, respectively) and the hours spent at home increased, indicating compliance with the measures. Increased time spent at home during the measures was associated with time spent at work before the measures (β= -0.87, 95% CI [-1.21,-0.53]) as well as with being retired vs employed (β= 2.32, 95% CI [1.70, 2.93]). The temporal network analysis indicated that most cases originated at work, while the synthetic human proximity networks adequately reproduced the observed SARS-CoV-2 spread. Exposome approaches (i.e., holistic characterization of the spatiotemporal variation of multiple exposures) would aid the comprehensive description of population-wide measures' impact and explore how behaviors and networks may shape SARS-CoV-2 transmission., (© 2022. The Author(s).)

Human proximity networks are temporal networks representing the close-range proximity among humans in a physical space. They have been extensively studied in the past 15 years as they are critical for understanding the spreading of diseases and information among humans. Here we address the problem of mapping human proximity networks into hyperbolic spaces. Each snapshot of these networks is often very sparse, consisting of a small number of interacting (i.e., non-zero degree) nodes. Yet, we show that the time-aggregated representation of such systems over sufficiently large periods can be meaningfully embedded into the hyperbolic space, using methods developed for traditional (non-mobile) complex networks. We justify this compatibility theoretically and validate it experimentally. We produce hyperbolic maps of six different real systems, and show that the maps can be used to identify communities, facilitate efficient greedy routing on the temporal network, and predict future links with significant precision. Further, we show that epidemic arrival times are positively correlated with the hyperbolic distance from the infection sources in the maps. Thus, hyperbolic embedding could also provide a new perspective for understanding and predicting the behavior of epidemic spreading in human proximity systems.