Delivering the Multisensory Experience of Dining-Out, for Those Dining-In, During the Covid Pandemic.

In many parts of the world, restaurants have been forced to close in unprecedented numbers during the various Covid-19 pandemic lockdowns that have paralyzed the hospitality industry globally. This highly-challenging operating environment has led to a rapid expansion in the number of high-end restaurants offering take-away food, or home-delivery meal kits, simply in order to survive. While the market for the home delivery of food was already expanding rapidly prior to the emergence of the Covid pandemic, the explosive recent growth seen in this sector has thrown up some intriguing issues and challenges. For instance, concerns have been raised over where many of the meals that are being delivered are being prepared, given the rise of so-called "dark kitchens." Furthermore, figuring out which elements of the high-end, fine-dining experience, and of the increasingly-popular multisensory experiential dining, can be captured by those diners who may be eating and drinking in the comfort of their own homes represents an intriguing challenge for the emerging field of gastrophysics research; one that the chefs, restaurateurs, restaurant groups, and even the food delivery companies concerned are only just beginning to get to grips with. By analyzing a number of the high-end fine-dining home food delivery options that have been offered (in the UK and in the US) in this narrative review, we highlight a number of promising directions for those wanting to optimize the at-home multisensory dining experience, wherever in the world they might be.

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Aging and the (Chemical) Senses: Implications for Food Behaviour Amongst Elderly Consumers.

The growing aging population are increasingly suffering from the negative health consequences of the age-related decline in their senses, especially their chemical senses. Unfortunately, however, unlike for the higher senses of vision and hearing, there is currently nothing that can be done to bring back the chemical senses once they are lost (or have started their inevitable decline). The evidence suggests that such chemosensory changes can result in a range of maladaptive food behaviours, including the addition of more salt and sugar to food and drink in order to experience the same taste intensity while, at the same time, reducing their overall consumption because food has lost its savour. Here, though, it is also important to stress the importance of the more social aspects of eating and drinking, given the evidence suggesting that a growing number of older individuals are consuming more of their meals alone than ever before. Various solutions have been put forward in order to try to enhance the food experience amongst the elderly, including everything from optimising the product-intrinsic food inputs provided to the remaining functional senses through to a variety of digital interventions. Ultimately, however, the aim has to be to encourage healthier patterns of food consumption amongst this rapidly-growing section of the population by optimising the sensory, nutritional, social, and emotional aspects of eating and drinking. An experimental dinner with the residents of one such home where nostalgic-flavoured healthy ice-creams were served is described.

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as "anti-cancer" with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these 'learned' interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84-90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a 'food map' with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.