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Spotted wing drosophila (SWD) causes significant economic loss in fruit crops to growers worldwide. There is immediate need for efficacious and selective monitoring tools that can detect infestations early. Previously, volatile organic compounds derived from apple were studied and a quinary chemical component blend (QB) was identified as the key SWD attractant in a blueberry orchard in the United States. This study's aim was to determine whether previously observed QB efficacy, selectivity, and early detection levels could be attained within raspberry and cherry fields in the USA and Europe. Results demonstrated that sticky trap baited QB dispenser provided earlier SWD detection potential than the usually adopted apple cider vinegar (ACV) trap. The number of SWD captured/trap by QB baited trapping systems was significantly lower than that of the ACV trap. However, percent SWD/trap of QB baited traps was same within cherry. Lower non-target capture will save farmer/grower's labor and time allocated to traps installation and drosophila species identification. Within the USA, SWD selectivity of QB baited liquid traps was consistently greater than sticky trap in raspberry field, suggesting that the QB dispenser can be an alternative to the standard ACV lure and that trap design could improve selectivity further. [ABSTRACT FROM AUTHOR]

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil's forecasts, we employed COVID-19 data from several European and U.S. states. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms conventional data-centric approaches, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants., (© 2024. The Author(s).)

Globally, maternal birth season affects fertility later in life. The purpose of this systematic literature review is to comprehensively investigate the birth season and female fertility relationship. Using PubMed, we identified a set of 282 relevant fertility/birth season papers published between 1972 and 2018. We screened all 282 studies and removed 131 non-mammalian species studies on fertility and 122 studies that were on non-human mammals. Our meta-analysis focused on the remaining 29 human studies, including twelve human datasets from around the world (USA, Europe, Asia). The main outcome was change in female fertility as observed by maternal birth month and whether this change was correlated with either temperature or rainfall. We found that temperature was either strongly correlated or anti-correlated in studies, indicating that another factor closely tied to temperature may be the culprit exposure. We found that rainfall only increases fertility in higher altitude locations (New Zealand, Romania, and Northern Vietnam). This suggests the possibility of a combined or multi-factorial mechanism underlying the female fertility - birth season relationship. We discuss other environmental and sociological factors on the birth season - female fertility relationship. Future research should focus on the role of birth season and female fertility adjusting for additional factors that modulate female fertility as discussed in this comprehensive review.