Effectiveness of current hygiene practices on minimization of Listeria monocytogenes in different mushroom production‐related environments

peer-reviewedBackground: The commercial production of Agaricus bisporus is a three stage process: 1) production of compost, also called “substrate”; 2) production of casing soil; and 3) production of the mushrooms. Hygiene practices are undertaken at each stage: pasteurization of the substrate, hygiene practices applied during the production of casing soil, postharvest steam cookout, and disinfection at the mushroom production facilities. However, despite these measures, foodborne pathogens, including Listeria monocytogenes, are reported in the mushroom production environment. In this work, the presence of L. monocytogenes was evaluated before and after the application of hygiene practices at each stage of mushroom production with swabs, samples of substrate, casing, and spent mushroom growing substrates. Results: L. monocytogenes was not detected in any casing or substrate sample by enumeration according to BS EN ISO 11290-2:1998. Analysis of the substrate showed that L. monocytogenes was absent in 10 Phase II samples following pasteurization, but was then present in 40% of 10 Phase III samples. At the casing production facility, 31% of 59 samples were positive. Hygiene improvements were applied, and after four sampling occasions, 22% of 37 samples were positive, but no statistically significant difference was observed (p > .05). At mushroom production facilities, the steam cookout process inactivated L. monocytogenes in the spent growth substrate, but 13% of 15 floor swabs at Company 1 and 19% of 16 floor swabs at Company 2, taken after disinfection, were positive. Conclusion: These results showed the possibility of L. monocytogenes recontamination of Phase III substrate, cross-contamination at the casing production stage and possible survival after postharvest hygiene practices at the mushroom growing facilities. This information will support the development of targeted measures to minimize L. monocytogenes in the mushroom industry.Food Institutional Research Measur

Reduced Rate of Hospital Admissions for ACS during Covid-19 Outbreak in Northern Italy

To address the coronavirus (Covid-19) pandemic,1 strict social containment measures have been adopted worldwide, and health care systems have been reorganized to cope with the enormous increase in the numbers of acutely ill patients.2,3 During this same period, some changes in the pattern of hospital admissions for other conditions have been noted. The aim of the present analysis is to investigate the rate of hospital admissions for acute coronary syndrome (ACS) during the early days of the Covid-19 outbreak

Music Recommendation via Hypergraph Embedding

In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to support users in browsing these massive collections of multimedia data according to their preferences and needs. In this context, the modeling of entities and their complex relationships (e.g., users listening to topic-based songs or authors creating different releases of their lyrics) represents the key challenge to improve the recommendation and maximize the users' satisfaction. To this end, this is the first study to leverage the high representative power of hypergraph data structures in combination with modern graph machine learning techniques in the context of music recommendation. Specifically, we propose hypergraph embeddings for music recommendation (HEMR), a novel framework for song recommendation based on hypergraph embedding. The hypergraph data model allows us to represent seamlessly all the possible and complex interactions between users and songs with the related characteristics; meanwhile, embedding techniques provide a powerful way to infer the user-song similarities by vector mapping. We have experimented the effectiveness and efficiency of our approach with respect to the state-of-the-art most recent music recommender systems, exploiting the Million Song dataset. The results show that HEMR significantly outperforms other state-of-the-art techniques, especially in scenarios where the cold-start problem arises, thus making our system a suitable solution to embed within a music streaming platform