Towards the definition of a detailed transcriptomic map of grape berry development

New Plant Breeding Techniques (NPBTs) have arisen with the objective of surmounting the constraints inherent in conventional breeding methodologies, thereby enhancing plant resilience against both biotic and abiotic stresses. To date the application of genome editing in grapevine is still limited by the necessity to overcome recalcitrance to produce embryogenic calli and to regenerate plants. In our studies, we developed a smart and versatile genetic transformation system carrying all the most promising features of different genome editing approaches. In specific, we joined the GRF-GIF expression to improve regeneration, the systemic movement of the editing transcripts through tRNA-like sequences (TLS) and the cisgenic-like approach to remove transgenes.

AIM: Patterns in data obtained from wine chemical and sensory evaluations are difficult to infer using classical statistics.

Unmanned Aerial Vehicles (UAVs) are increasingly used to monitor canopy structure and vineyard performance. Compared with traditional remote sensing platforms (e.g. aircraft and satellite), UAVs offer a higher operational flexibility and can acquire ultra-high resolution images in formats such as true color red, green and blue (RGB) and multispectral. Using photogrammetry, 3D vineyard models and normalized difference vegetation index (NDVI) maps can be created from UAV images and used to study the structure and health of grapevine canopies. However, there is a lack of comparison between UAV-based images and ground-based measurements, such as leaf area index (LAI) and canopy porosity.

The Intergovernmental Panel on Climate Change (IPCC) forecasts an increase in global temperature and a decrease in relative humidity (RH) in the coming decades, which may have implications for berry ripening and composition.

The main goal of the current study is the development of a cost-effective and easy-to-use method suitable for use in the laboratory of commercial wineries to analyze wine aroma. Additionally, this study attempted to establish a prediction model for wine quality gradings based on their aroma, which could reveal the important aroma compounds that correlate well with different grades of perceived quality METHODS: Parameters of the SHS−GC−IMS instrument were first optimized to acquire the most desirable chromatographic resolution and signal intensities. Method stability was then exhibited by repeatability and reproducibility. Subsequently, compound identification was conducted. After method development, a total of 143 end-ferment wine samples of three different quality gradings from vintage 2020 were analyzed with the SHS−GC−IMS instrument. Six machine learning methods were employed to process the results and construct a quality prediction model. Techniques that aim to explain the model to extract useful insights were also applied.