The application of plant beneficial microorganisms is widely accepted as an efficient alternative to chemical fertilizers and pesticides. It was shown that annually, mycorrhizal fungi and nitrogen-fixing bacteria are responsible for 5 to 80% of all nitrogen, and up to 75% of P plant acquisition. However, while bacteria are the most studied soil microorganisms and most frequently reported in the scientific literature, the role of fungi is relatively understudied, although they are the primary organic matter decomposers and govern soil carbon and other elements, including P-cycling. Many fungi can solubilize insoluble phosphates or facilitate P-acquisition by plants and, therefore, form an important part of the commercial microbial products, with Aspergillus, Penicillium and Trichoderma being the most efficient. In this paper, the role of fungi in P-solubilization and plant nutrition will be presented with a special emphasis on their production and application. Although this topic has been repeatedly reviewed, some recent views questioned the efficacy of the microbial P-solubilizers in soil. Here, we will try to summarize the proven facts but also discuss further lines of research that may clarify our doubts in this field or open new perspectives on using the microbial and particularly fungal P-solubilizing potential in accordance with the principles of the sustainability and circular economy.

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.