Phenolic composition of red grapes grown in Southern Italy

The phenolic composition of red grapes native to Southern Italy (Aglianico, Carignano, Frappato, Gaglioppo, Negro Amaro, Nero d'Avola, Primitivo, Tintilia, and Uva di Troia) and an "international" grape (Cabernet Sauvignon) introduced into the Apulia region were investigated. Results showed that these cultivars could be divided into two groups on the basis of both their anthocyanin content and the presence of ortho-hydroxylated groups. Further differences regarded the ratio between flavans reacting with vanillin and proanthocyanidins. The anthocyanin profile of the skin of Negro Amaro, Primitivo and Uva di Troia grapes was found to be a specific characteristic of the grape variety which was affected only slightly by the place of growing. The different phenolic composition of the cultivars determines a different aptitude to wine production. The Cabernet Sauvignon grapes, due to their high concentration in polyphenolic substances, could be added to the native grape varieties in order to produce wines with a more complex aroma

Interannual-to-multidecadal hydroclimate variability and its sectoral impacts in northeastern Argentina

This study examines the joint variability of precipitation, river streamflow and temperature over northeastern Argentina; advances the understanding of their links with global SST forcing; and discusses their impacts on water resources, agriculture and human settlements. The leading patterns of variability, and their nonlinear trends and cycles are identified by means of a principal component analysis (PCA) complemented with a singular spectrum analysis (SSA). Interannual hydroclimatic variability centers on two broad frequency bands: one of 2.5–6.5 years corresponding to El Niño Southern Oscillation (ENSO) periodicities and the second of about 9 years. The higher frequencies of the precipitation variability (2.5–4 years) favored extreme events after 2000, even during moderate extreme phases of the ENSO. Minimum temperature is correlated with ENSO with a main frequency close to 3 years. Maximum temperature time series correlate well with SST variability over the South Atlantic, Indian and Pacific oceans with a 9-year frequency. Interdecadal variability is characterized by low-frequency trends and multidecadal oscillations that have induced a transition from dryer and cooler climate to wetter and warmer decades starting in the mid-twentieth century. The Paraná River streamflow is influenced by North and South Atlantic SSTs with bidecadal periodicities. The hydroclimate variability at all timescales had significant sectoral impacts. Frequent wet events between 1970 and 2005 favored floods that affected agricultural and livestock productivity and forced population displacements. On the other hand, agricultural droughts resulted in soil moisture deficits that affected crops at critical growth stages. Hydrological droughts affected surface water resources, causing water and food scarcity and stressing the capacity for hydropower generation. Lastly, increases in minimum temperature reduced wheat and barley yields

Enhancing PFI Prediction with GDS-MIL: A Graph-Based Dual Stream MIL Approach

Whole-Slide Images (WSI) are emerging as a promising resource for studying biological tissues, demonstrating a great potential in aiding cancer diagnosis and improving patient treatment. However, the manual pixel-level annotation of WSIs is extremely time-consuming and practically unfeasible in real-world scenarios. Multi-Instance Learning (MIL) have gained attention as a weakly supervised approach able to address lack of annotation tasks. MIL models aggregate patches (e.g., cropping of a WSI) into bag-level representations (e.g., WSI label), but neglect spatial information of the WSIs, crucial for histological analysis. In the High-Grade Serous Ovarian Cancer (HGSOC) context, spatial information is essential to predict a prognosis indicator (the Platinum-Free Interval, PFI) from WSIs. Such a prediction would bring highly valuable insights both for patient treatment and prognosis of chemotherapy resistance. Indeed, NeoAdjuvant ChemoTherapy (NACT) induces changes in tumor tissue morphology and composition, making the prediction of PFI from WSIs extremely challenging. In this paper, we propose GDS-MIL, a method that integrates a state-of-the-art MIL model with a Graph ATtention layer (GAT in short) to inject a local context into each instance before MIL aggregation. Our approach achieves a significant improvement in accuracy on the “Ome18” PFI dataset. In summary, this paper presents a novel solution for enhancing PFI prediction in HGSOC, with the potential of significantly improving treatment decisions and patient outcomes

Spatiotemporal variability of extreme precipitation events and their impacts on soil moisture and water table depth in Argentina’s core crop region

This study reassesses the spatiotemporal variability and the changes of extreme precipitation events (EPEs) and examines the response of soil moisture and water table depth to EPEs in Argentina’s core crop region. Extreme events are analysed using standardised nonparametric indices applied to precipitation, soil moisture, and groundwater. The temporal variability of EPEs exhibits a decadal cycle and interannual modes with dominant frequencies between 2.5 and 4 years related to El Niño-Southern Oscillation (ENSO) periodicities. The soil moisture and water table depth respond to precipitation variability replicating both decadal and interannual EPE variability. The response of soil moisture to EPEs attenuates in time as soil depth increases. Water table depth responds to EPEs with an average delay of 10 months. In recent decades, soil moisture increases (water table depth becomes shallower) rapidly when reacting to excess precipitation, while soil moisture decreases (water table depth deepens) slowly during and after drought events.</p