Claudio Caprioli, Saumitra Kulkarni, Federico Battiston, Iacopo Iacopini, Andrea Santoro, Vito Latora

Investigating how different ingredients are combined in popular dishes is crucial to reveal the fundamental principles behind the formation of food preferences. Here, we use data from food repositories and network analysis to characterize worldwide cuisines. In our framework, each cuisine is represented as a network, where nodes correspond to ingredient types and weighted links describe how frequently pairs of ingredient types appear together in recipes. The networks of ingredient combinations reveal cuisine-specific patterns, highlighting similarities and differences in gastronomic preferences across different world regions. We find that popular ingredients, recurrent combinations, and the way they are organized within the backbone of the network provide a unique fingerprint for each cuisine. Hence, we demonstrate that networks of ingredient combinations are able to cluster global cuisines into meaningful geo-cultural groups, and can also be used to train models to uniquely identify a cuisine from a subset of its recipes. Our study advances our understanding of food combinations and helps uncover the geography of taste, paving the way for the creation of new and innovative recipes.

Read the full article at: arxiv.org

Bibandhan Poudyal , Diogo Pacheco , Marcos Oliveira , Zexun Chen , Hugo S. Barbosa , Ronaldo Menezes and Gourab Ghoshal

Royal Society Open Science

September 2024 Volume 11Issue 9

Human travelling behaviours are markedly regular, to a large extent predictable, and mostly driven by biological necessities and social constructs. Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual preferences and choices, through social groups and households, all the way to the global scale, such as mobility restrictions in response to external shocks such as pandemics. In this work, we explore how temporal, activity and location variations in individual-level mobility—referred to as predictability states—carry a large degree of information regarding the nature of mobility regularities at the population level. Our findings indicate the existence of contextual and activity signatures in predictability states, suggesting the potential for a more nuanced approach to estimating both short-term and higher-order mobility predictions. The existence of location contexts, in particular, serves as a parsimonious estimator for predictability patterns even in the case of low resolution and missing data.

Read the full article at: royalsocietypublishing.org

Franz Kuchling, Isha Singh, Mridushi Daga, Susan Zec, Alexandra Kunen, and Michael Levin

Learning and a spectrum of other behavioral competencies allow organisms to rapidly adapt to dynamically changing environmental variations. The emerging field of diverse intelligence seeks to understand what systems, besides ones with complex brains, exhibit these capacities. Here, we tested predictions of a general computational framework based on the free energy principle in neuroscience but applied to aneural biological process as established previously, by demonstrating and manipulating pattern recognition in a simple aneural organism, the green algae Volvox. Our studies of the adaptive photoresponse in Volvox reveal that aneural organisms can distinguish between patterned and randomized inputs and indicate how this is achieved mechanistically. We show that the phototactic response in Volvox adapts more readily to regular light pulse patterns than to irregular ones, thus exhibiting a crucial component of basal intelligence – generalization: the ability to recognize patterns in input stimuli. Randomized electric shocks reduced the ability of Volvox to maintain adaptive phototaxis significantly more than regularly applied electric shocks, providing first evidence for a stress effect of randomized input patterns in a primitive organism. Moreover, we detected memory in Volvox – a persistence of movement towards past light stimulation through their phototactic orientation, another foundational aspect of neural-like primitive cognition. Combined, these data reveal that Volvox exhibit a capacity for pattern recognition consistent with uncertainty minimization. The ability of algae to be surprised and distinguish random events that do not meet expected patterns further expands neurobiological concepts beyond neurons. These methods can likely be translated to the study and manipulation of basal cognition in many other living systems.

Read the full article at: osf.io