Valorization of traditional Italian walnut (Juglans regia L.) production: genetic, nutritional and sensory characterization of locally grown varieties in the Trentino region

15openYesJuglans regia (L.) is cultivated worldwide for its nutrient-rich nuts. In Italy, despite the growing demand, walnut cultivation has gone through a strong decline in recent decades, which led to Italy being among the top five net importing countries. To promote the development of local high-quality Italian walnut production, we devised a multidisciplinary project to highlight the distinctive traits of three varieties grown in the mountainous region Trentino (northeast of Italy): the heirloom ‘Bleggiana’, a second local accession called local Franquette and the French cultivar ‘Lara’, recently introduced in the local production to increase yield. The genetic characterization confirmed the uniqueness of ‘Bleggiana’ and revealed local Franquette as a newly described autochthonous variety, thus named ‘Blegette’. The metabolic profiles highlighted a valuable nutritional composition of the local varieties, richer in polyphenols and with a lower ω-6/ω-3 ratio than the commercial ‘Lara’. ‘Blegette’ obtained the highest preference scores from consumers for both the visual aspect and tasting; however, the volatile organic compound profiles did not discriminate among the characterized cultivars. The described local varieties represent an interesting reservoir of walnut genetic diversity and quality properties, which deserve future investigation on agronomically useful traits (e.g., local adaptation and water usage) for a high-quality and sustainable production.Di Pierro, Erica A.; Franceschi, Pietro; Endrizzi, Isabella; Farneti, Brian; Poles, Lara; Masuero, Domenico; Khomenko, Iuliia; Trenti, Francesco; Marrano, Annarita; Vrhovsek, Urska; Gasperi, Flavia; Biasioli, Franco; Guella, Graziano; Bianco, Luca; Troggio, MichelaDi Pierro, E.A.; Franceschi, P.; Endrizzi, I.; Farneti, B.; Poles, L.; Masuero, D.; Khomenko, I.; Trenti, F.; Marrano, A.; Vrhovsek, U.; Gasperi, F.; Biasioli, F.; Guella, G.; Bianco, L.; Troggio, M

Towards an Informed CNN for Bone SR-microCT Image Classification with an Unsupervised Patched-based Image Clustering

: Bone microscale differences cannot be readily recognizable to humans from Synchrotron Radiation micro-Computed Tomography (SR-microCT) images. Premises are possible with Deep Learning (DL) imaging analysis. Despite this, more attention to high-level features leads models to require help identifying relevant details to support a decision. Within this context, we propose a method for classifying healthy, osteoporotic, and COVID-19 femoral heads SR-microCT images informing a vgg16 about the most subtle microscale differences using unsupervised patched-based clustering. Our strategy allows achieving up to 9.8% accuracy improvement in classifying healthy from osteoporotic images over uninformed methods, while 59.1% of accuracy between osteoporosis and COVID-19.Clinical relevance-We established a starting point for classifying healthy, osteoporotic, and COVID-19 femoral heads from SR-microCTs with human non-discriminative features, with 60.91% accuracy in healthy-osteporotic image classification

A Graph Machine Learning approach to Automatic Dementia Detection

Dementia is a term used to refer to a wide range of diseases that cause a decline in cognitive abilities. This decline is severe enough to impair daily life and it is extremely complex to diagnose in its early stages. In recent years multiple Natural Language Processing solutions have been proposed to automatically detect dementia. One of the main approaches to this problem is based on extracting manually engineered features from a set of patients' conversations and feeding them to traditional Machine Learning models. These features can be divided into very different groups, and we can define specific relations that connect one feature to the other. Thus, we introduce a new way to approach the problem by organizing all the extracted features in a graph structure and using Graph Machine Learning to detect dementia. We validate our method using a well-established score regression task and a newly proposed multi-class classification task. This new task is based on the mapping between the Mini-Mental State Examination score and multiple dementia severity levels. Compared to traditional Machine Learning, our Graph Machine learning technique achieves a relative increase in performance between 2.9% and 8% for the regression task, and between 4.4% and 7.9% for the classification task

The meaning of a “good nurse” in pediatric care: a concept analysis

ABSTRACT Objective: to analyze the attributes, antecedents and consequences of the concept a “good nurse” in the context of Pediatrics. Method: concept analysis study based on Rodgers’ evolutionary method. Theoretical stage consisted of searching for articles in the CINAHL, Embase and Pubmed databases and a practical stage of semi-structured interviews with pediatric nurses. The final analysis unified the two stages by categories of antecedents, attributes and consequences of the concept. Results: 20 articles and 10 interviews were analyzed revealing as antecedents aspects related to education, scientific development and ethical-moral skills and values. Responsibility, compassion, honesty and advocacy stand out as attributes of the “good nurse.” The consequences describe implications for children and families, as well as for professionals. Final Consideration: the analysis of the concept of the “good nurse” allowed us to clarify fundamental aspects for the execution of good practices, establishing parameters for investment in professional development programs