Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants

Ornamental plant production constitutes an important sector of the horticultural industry worldwide and fungal infections, that dramatically affect the aesthetic quality of plants, can cause serious economic and crop losses. The need to reduce the use of pesticides for controlling fungal outbreaks requires the development of new sustainable strategies for pathogen control. In particular, early and accurate large-scale detection of occurring symptoms is critical to face the ambitious challenge of an effective, energy-saving, and precise disease management. Here, the new trends in digital-based detection and available tools to treat fungal infections are presented in comparison with conventional practices. Recent advances in molecular biology tools, spectroscopic and imaging technologies and fungal risk models based on microclimate trends are examined. The revised spectroscopic and imaging technologies were tested through a case study on rose plants showing important fungal diseases (i.e., spot spectroscopy, hyperspectral, multispectral, and thermal imaging, fluorescence sensors). The final aim was the examination of conventional practices and current e-tools to gain the early detection of plant diseases, the identification of timing and spacing for their proper management, reduction in crop losses through environmentally friendly and sustainable production systems. Moreover, future perspectives for enhancing the integration of all these approaches are discussed

Análise de Sobrevivência: aplicação num estudo de podridão parda em pêssegos com o uso do ambiente R

Este material aborda uma aplicação da técnica estatística denominada ”Análise de Sobrevivência” no estudo da podridão parda em pêssegos. Apesar de bem conhecida e muito aplicada na área da medicina humana, esta técnica ainda tem poucas aplicações em estudos agronômicos. No estudo de doen¸cas de plantas, uma situação comum é aquela em que se avalia a ocorrência da doença e o tempo para sua ocorrência em uma população de indivíduos. Entretanto, o tempo de observação raramente tem distribuição normal e os dados podem ser censurados, ou seja, o estudo pode terminar antes que todos os indivíduos avaliados sofram o evento de interesse, observando-se parcialmente a resposta. Frequentemente, dados desta natureza são submetidos à análise estatística convencional com exclusão de observações censuradas e transformação de dados. A análise de sobrevivência é aplicada em situações em que o tempo até a ocorrência de um evento é o objeto de interesse. Dados dessa natureza são rotineiramente coletados em epidemiologia de doenças de plantas, embora aplicaçõees da técnica sejam pouco comuns. Neste contexto, o objetivo deste boletim é descrever as principais técnicas em análise de sobrevivência e sua implementação em ambiente R, com aplicação num estudo sobre sintomas de podridão parda em pêssegos causada por Monilinia fructicola. Espera-se que este material auxilie pesquisadores nas diversas áreas, utilizando uma técnica adequada à natureza dos dados de tempo-ocorrência, além de disponiblizar códigos em ambiente R para facilitar a análise e discussão que colabore na interpretação dos resultados

Genetic characterization of juvenile sudden cardiac arrest and death in Tuscany: The ToRSADE registry

BackgroundSudden cardiac arrest (SCA) in young people represents a dramatic event, often leading to severe neurologic outcomes or sudden cardiac death (SCD), and is frequently caused by genetic heart diseases. In this study, we report the results of the Tuscany registry of sudden cardiac death (ToRSADE) registry, aimed at monitoring the incidence and investigating the genetic basis of SCA and SCD occurring in subjects < 50 years of age in Tuscany, Italy.Methods and resultsCreation of the ToRSADE registry allowed implementation of a repository for clinical, molecular and genetic data. For 22 patients, in whom a genetic substrate was documented or suspected, blood samples could be analyzed; 14 were collected at autopsy and 8 from resuscitated patients after SCA. Next generation sequencing (NGS) analysis revealed likely pathogenetic (LP) variants associated with cardiomyopathy (CM) or channelopathy in four patients (19%), while 17 (81%) carried variants of uncertain significance in relevant genes (VUS). In only one patient NGS confirmed the diagnosis obtained during autopsy: the p.(Asn480Lysfs*20) PKP2 mutation in a patient with arrhythmogenic cardiomyopathy (AC).ConclusionSystematic genetic screening allowed identification of LP variants in 19% of consecutive patients with SCA/SCD, including subjects carrying variants associated with hypertrophic cardiomyopathy (HCM) or AC who had SCA/SCD in the absence of structural cardiomyopathy phenotype. Genetic analysis combined with clinical information in survived patients and post-mortem evaluation represent an essential multi-disciplinary approach to manage juvenile SCD and SCA, key to providing appropriate medical and genetic assistance to families, and advancing knowledge on the basis of arrhythmogenic mechanisms in inherited cardiomyopathies and channelopathies

Small vessel disease and biomarkers of endothelial dysfunction after ischaemic stroke

Abstract Introduction: Although pathogenesis of small vessel disease is poorly understood, increasing evidence suggests that endothelial dysfunction may have a relevant role in development and progression of small vessel disease. In this crosssectional study, we investigated the associations between imaging signs of small vessel disease and blood biomarkers of endothelial dysfunction at two different time points in a population of ischaemic stroke patients. Patients and methods: In stroke patients treated with intravenous thrombolysis, we analysed blood levels of von Willebrand factor, intercellular adhesion molecule-1, vascular cell adhesion molecule-1 and vascular endothelial growth factor. Three reviewers independently assessed small vessel disease features using computed tomography. At baseline and 90 days after the index stroke, we tested the associations between single and combined small vessel disease features and levels of blood biomarkers using linear regression analysis adjusting for age, sex, hypertension, diabetes, smoke. Results: A total of 263 patients were available for the analysis. Mean age (SD) was 69 (13) years, 154 (59%) patients were male.We did not find any relation between small vessel disease and endothelial dysfunction at baseline. At 90 days, leukoaraiosis was independently associated with intercellular adhesionmolecule-1 (b¼0.21; p¼0.016) and vascular cell adhesionmolecule- 1 (b¼0.22; p¼0.009), and lacunes were associated with vascular endothelial growth factor levels (b¼0.21; p¼0.009) whereas global small vessel disease burden was associated with vascular endothelial growth factor (b¼0.26; p¼0.006). Discussion: Leukoaraiosis and lacunes were associated with endothelial dysfunction, which could play a key role in pathogenesis of small vessel disease

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

SIMPLE SUMMARY: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs. ABSTRACT: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs

Rose: A new host plant of Fusarium clavum (F. incarnatum-equiseti species complex 5) causing brown spot of petals

Roses (Rosa spp.) are widely cultivated in Italy for ornamental and aesthetic purposes. In autumn 2020, petal brown spots on rose buds especially on petals were observed in outdoor potted plants in Tuscany (Italy), causing aesthetic damage compromising marketability of cut flowers. A study about morphological and phylogenetic features of the disease-associated fungus, followed by a pathogenicity assay according to Koch's postulate, was carried out. The recently described Fusarium clavum (FIESC 5) was found to be the causal agent of petal brown spot disease on rose buds in Tuscany, Italy

Survival analysis: a tool in the study of post-harvest diseases in peaches

Survival analysis is applied when the time until the occurrence of an event is of interest. Such data are routinely collected in plant diseases, although applications of the method are uncommon. The objective of this study was to use two studies on post-harvest diseases of peaches, considering two harvests together and the existence of random effect shared by fruits of a same tree, in order to describe the main techniques in survival analysis. The nonparametric Kaplan-Meier method, the log-rank test and the semi-parametric Cox's proportional hazards model were used to estimate the effect of cultivars and the number of days after full bloom on the survival to the brown rot symptom and the instantaneous risk of expressing it in two consecutive harvests. The joint analysis with baseline effect, varying between harvests, and the confirmation of the tree effect as a grouping factor with random effect were appropriate to interpret the phenomenon (disease) evaluated and can be important tools to replace or complement the conventional analysis, respecting the nature of the variable and the phenomenon