Artificial intelligence (AI)-based protein modeling for the interpretation of grapevine genetic variants

Auric infinity Technology introduces three new patented products designated for fermentation, aging and bottling without added additives and preservatives that have never been used in the winemaking industry.

Global warming is responsible for a lack of organic acid in grape berries, leading to wines with higher pH and lower titrable acidity. The chemical, microbiological and organoleptic equilibriums are impacted by this change of organic acid concentration. It is common practice to acidify the wine in order to prevent these imbalances that can lead to wine defects and early spoilage. Tartaric acid (TA) is most commonly used by winemaker for wine acidification purposes. Fumaric acid (FA), which is authorized by the OIV in its member states for the inhibition of malolactic fermentation, could also be used as a potential acidification candidate since it has a better acidifying power than tartaric acid.

Deleterious mutations that severely reduce population fitness are rapidly removed from the gene pool by purifying selection. However, evolutionary drivers such as genetic drift brought about by demographic bottlenecks may comprise its efficacy by allowing deleterious mutations to accumulate, thereby limiting the adaptive potential of populations. Moreover, positive selection can hitchhike mildly deleterious mutations due to linkage caused by lack of recombination. Similarly, in the context of species domestication, artificial selection mimics these evolutionary processes, which can have undesirable consequences for production and resilience. In this study, we evaluated the extent of the accumulation of deleterious mutations and the magnitude of their effects (also known as genetic load) at the whole-genome scale for ca.

In the context of sustainable viticulture, modern and efficient techniques to determine water status are required to optimize irrigation practices. Proximal techniques such as thermography and spectroscopy have shown promising results. When these techniques are incorporated into mobile systems is possible to evaluate the water status on-the-go, offering the possibility to generate variability maps. However, in most cases, complex protocols of data acquisition and analysis are required. Also, the inherent physiological behaviour of the plants under certain water stress conditions needs to be considered. Therefore, the aim of this study was to evaluate the effect of scion-rootstock combinations on the performance of a predefined plant-based method based on proximal near-infrared (NIR) spectroscopy.

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