Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality.

The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability.

Echo state networks for the recognition of type 1 Brugada syndrome from conventional 12-LEAD ECG.

Artificial Intelligence (AI) applications and Machine Learning (ML) methods have gained much attention in recent years for their ability to automatically detect patterns in data without being explicitly taught rules. Specific features characterise the ECGs of patients with Brugada Syndrome (BrS); however, there is still ambiguity regarding the correct diagnosis of BrS and its differentiation from other pathologies. This work presents an application of Echo State Networks (ESN) in the Recurrent Neural Networks (RNN) class for diagnosing BrS from the ECG time series.

A machine learning approach to assess Sustainable Development Goals food performances: The Italian case.

In this study, we introduce an innovative application of clustering algorithms to assess and appraise Italy's alignment with respect to the Sustainable Development Goals (SDGs), focusing on those related to climate change and the agrifood market. Specifically, we examined SDG 02: Zero Hunger, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Change, to evaluate Italy's performance in one of its most critical economic sectors. Beyond performance analysis, we administered a questionnaire to a cross-section of the Italian populace to gain deeper insights into their awareness of sustainability in everyday grocery shopping and their understanding of SDGs.

A multi-modal machine learning approach to detect extreme rainfall events in Sicily.

In 2021 almost 300 mm of rain, nearly half of the average annual rainfall, fell near Catania (Sicily Island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. These phenomena are now very common in various countries all around the world: this is the reason why, detecting local extreme rainfall events is a crucial prerequisite for planning actions, able to reverse possibly intensified dramatic future scenarios.

A machine learning approach to analyse and predict the electric cars scenario: The Italian case.

The automotive market is experiencing, in recent years, a period of deep transformation. Increasingly stricter rules on pollutant emissions and greater awareness of air quality by consumers are pushing the transport sector towards sustainable mobility. In this historical context, electric cars have been considered the most valid alternative to traditional internal combustion engine cars, thanks to their low polluting potential, with high growth prospects in the coming years.

Modular Multi-Source Prediction of Drug Side-Effects With DruGNN.

Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants.

Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson's disease patients.

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated.

Modeling Brain-Heart Crosstalk Information in Patients with Traumatic Brain Injury.

Background: Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rate (HR) monitoring to assess the interplay between brain and heart. The goal of our study is to investigate events of simultaneous increases in HR and ICP and their relationship with patient mortality.

Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits.

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters.

Analysis of Cardio-Cerebral Crosstalk Events in an Adult Cohort from the CENTER-TBI Study.

Objective: In a previous study, we observed the presence of simultaneous increases in intracranial pressure (ICP) and the heart rate (HR), which we denominated cardio-cerebral crosstalk (CC), and we related the number of such events to patient outcomes in a paediatric cohort. In this chapter, we present an extension of this work to an adult cohort from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study.

Methods: We implemented a sliding window algorithm to detect CC events.

Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease.

Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents a common complication in ultra-rare disorders like AKU. This is the reason why we developed a comprehensive tool, called ApreciseKUre, able to collect AKU patients deriving data, to analyse the complex network among genotypic and phenotypic information and to get new insight in such multi-systemic disease.

A geroscience approach for Parkinson's disease: Conceptual framework and design of PROPAG-AGEING project.

Advanced age is the major risk factor for idiopathic Parkinson's disease (PD), but to date the biological relationship between PD and ageing remains elusive. Here we describe the rationale and the design of the H2020 funded project "PROPAG-AGEING", whose aim is to characterize the contribution of the ageing process to PD development. We summarize current evidences that support the existence of a continuum between ageing and PD and justify the use of a Geroscience approach to study PD.

Unsupervised stratification in neuroimaging through deep latent embeddings.

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditional dichotomic categories are not helpful to describe the complexity and large heterogeneity of symptoms across many and overlapping clinical phenotypes. With the advent of 'big' multimodal neuroimaging databases, data-driven stratification of the wide spectrum of healthy human physiology or disease based on neuroimages is theoretically become possible.

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease.

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations.

Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU.

Simultaneous Transients of Intracranial Pressure and Heart Rate in Traumatic Brain Injury: Methods of Analysis.

Objectives: The detection of increasing intracranial pressure (ICP) is important in preventing secondary brain injuries. Before mean ICP increases critically, transient ICP elevations may be observed. We have observed ICP transients of less than 10 min duration ,which occurred simultaneously with transient increases in heart rate (HR).

Predictors of length of stay in psychiatric inpatient units: Does their effect vary across countries?

Background: Previous studies in individual countries have identified inconsistent predictors of length of stay (LoS) in psychiatric inpatient units. This may reflect methodological inconsistencies across studies or true differences of predictors. In this study we assessed predictors of LoS in five European countries and explored whether their effect varies across countries.

DrugClust: A machine learning approach for drugs side effects prediction.

Background: Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects.

Methods: In this paper we present DrugClust, a machine learning algorithm for drugs side effects prediction.

A multiplex network approach for the analysis of intracranial pressure and heart rate data in traumatic brain injured patients.

Background: We present a multiplex network model for the analysis of Intracranial Pressure (ICP) and Heart Rate (HR) behaviour after severe brain traumatic injuries in pediatric patients. The ICP monitoring is of vital importance for checking life threathening conditions, and understanding the behaviour of these parameters is crucial for a successful intervention of the clinician. Our own observations, exhibit cross-talks interaction events happening between HR and ICP, i.

Histopathological and ultrastructural studies of a Henneguya species (Myxozoa: Myxosporea) infesting the intestine of Clarias gariepinus from Benin (West Africa).

Fish culture is the best alternative to fill the gap between natural fish catches and estimated needs of populations in animal protein consumption. In West Africa, this goal required to have suitable fishes for aquaculture which are Clariidae and Tilapia. Clarias gariepinus (Clariidae) fetches a higher price than tilapias as it can be sold alive at the market but a high infestation by Henneguya leads to decrease this commercial value.

Local nasal immunotherapy with extract in powder form is effective and safe in grass pollen rhinitis: a double-blind study.

Background: Local nasal immunotherapy has been studied, by means of an extract in powder form, in patients with allergic rhinitis caused by grass pollen.

Methods: Thirty-two patients allergic to grass were studied for 37 weeks in a double-blind controlled trial. Subjects were selected on the basis of a positive history, skin test result, RAST finding, and result of intranasal challenge to grass pollen.

Local nasal immunotherapy for birch allergic rhinitis with extract in powder form.

Background: Traditional subcutaneous immunotherapy has been proved effective in birch pollenosis. It has, however, some drawbacks as systemic reactions, which are rare but important. Local nasal immunotherapy (LNIT) represents a potential safer route of allergen administration.

Manual ELISA assays on tPA and PAI-1 automated on Novapath I.S. preliminary report.

Tissue plasminogen activator (tPA) and plasminogen activator inhibitor 1 (PAI-1) have been pointed out as markers for deep venal thrombosis, acute myocardial infarction and several thrombotic disorders. This paper reports automation on Novapath I.S.