SPATIAL ANALYSIS OF PLANT AGRI-BIODIVERSITY IN POLLINO NATIONAL PARK

Spatial analysis of the in situ conserved plant landraces in Pollino National Park was carried out from 2009 to 2011. The sampling design, based on a standard landscape grid, captured the whole range of plant genetic resources monitored at a pluri-taxon level. Both old fruit trees, wines (I phase; see www.biodiversitapollino.it) and annual herbaceous plants (II phase) were monitored. Overall 119 georeferenced sampling units, each with a visible radius of 200-250 m represented the rural landscape of 21 municipalities in Basilicata and 3 in Calabria. Overall 41 different woody species comprising 519 traditional biotypes and 54 herbaceous species with 137 traditional cultivars were scored. Cultivar/species ratio is 10: 1 for woody plants and 3: 1 for herbaceous species. Diversity at the sub-specific level, averaged across the whole landscape was: Margalef = 80.51; Menhinick = 9.51; Shannon = 5.55; Simpson = 0.99; Briliouin = 5.36. Landscape units with highest genetic diversity (species and landrace richness) were highlighted within a heterogeneous mosaic of cultivar richness distribution according to ecology and rural settlments. Linear regression (R2= 0.78;r= 0.43) between herbaceous cultivars richness vs old fruit trees richness confirmed that agribiodiversity is spatially conserved in landscape production units based on multi species rather than mono species (e.g. specialized) agro-ecosystems. In addition, the Colombian introductions (bean, potato, maize, pumpkin, tomato, and chili) increased species richness (R2 = 0.80; r= 0.56) – without any displacement effect – within the landscape units already performing as a realized niche for the pre-colombian species (apple, pear, wheat, legumes, etc.) The core area of Mercure catchment basin – a realized niche for both pre and post Colombian species – connected with few units, each a-priori sized 4 x 4 Km, depicts the Pollino National Park agri-biodiversity genetic reserve

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the “black-box” nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients’ risks and necessary therapy adjustments due to changes in disease progression and/or therapy response