A Bayesian hierarchical model for spot fluorescence in microarrays

Microarray experiments are characterized by the presence of many sources of experimental bias and a remarkably large technical variability. The assessment of differential expression for genes transcribed into a small number of mRNA copies heavily depends on the proper quantification of background fluorescence within spot. The rough model `observed = hybridization plus background\u27 fluorescence is at first reformulated at spot level, then it is embedded into a Bayesian hierarchical model suited for fitting control spots. The novelties of the approach include the background correction performed on the latent mean of replicated spots, and an explicit model for outlying observations at low fluorescence values in which the probability of occurrence and their magnitude depend on the background fluorescence intensity. The analysis of unpublished data from a maize ear tissues experiment confirms the feasibility of MCMC inferences as regard the computational burden

Bayesian models of thermal and pluviometric time series in the Fucino plateau

This work was developed within the Project Metodologie e sistemi integrati per la qualificazione di produzioni orticole del Fucino (Methodologies and integrated systems for the classification of horticultural products in the Fucino plateau), sponsored by the Italian Ministry of Education, University and Research, Strategic Projects, Law 448/97. Agro-system managing, especially if necessary to achieve high quality in speciality crops, requires knowledge of main features and intrinsic variability of climate. Statistical models may properly summarize the structure existing behind the observed variability, furthermore they may support the agronomic manager by providing the probability that meteorological events happen in a time window of interest. More than 30 years of daily values collected in four sites located on the Fucino plateau, Abruzzo region, Italy, were studied by fitting Bayesian generalized linear models to air temperature maximum /minimum and rainfall time series. Bayesian predictive distributions of climate variables supporting decision-making processes were calculated at different timescales, 5-days for temperatures and 10-days for rainfall, both to reduce computational efforts and to simplify statistical model assumptions. Technicians and field operators, even with limited statistical training, may exploit the model output by inspecting graphs and climatic profiles of the cultivated areas during decision-making processes. Realizations taken from predictive distributions may also be used as input for agro-ecological models (e.g. models of crop growth, water balance). Fitted models may be exploited to monitor climatic changes and to revise climatic profiles of interest areas, periodically updating the probability distributions of target climatic variables. For the sake of brevity, the description of results is limited to just one of the four sites, and results for all other sites are available as supplementary information

Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering

Research involving diverse but related data sets, where associations between covariates and outcomes may vary, is prevalent in various fields including agronomic studies. In these scenarios, hierarchical models, also known as multilevel models, are frequently employed to assimilate information from different data sets while accommodating their distinct characteristics. However, their structure extend beyond simple heterogeneity, as variables often form complex networks of causal relationships. Bayesian networks (BNs) provide a powerful framework for modelling such relationships using directed acyclic graphs to illustrate the connections between variables. This study introduces a novel approach that integrates random effects into BN learning. Rooted in linear mixed-effects models, this approach is particularly well-suited for handling hierarchical data. Results from a real-world agronomic trial suggest that employing this approach enhances structural learning, leading to the discovery of new connections and the improvement of improved model specification. Furthermore, we observe a reduction in prediction errors from 28\% to 17\%. By extending the applicability of BNs to complex data set structures, this approach contributes to the effective utilisation of BNs for hierarchical agronomic data. This, in turn, enhances their value as decision-support tools in the field.Comment: 28 pages, 5 figure

Longitudinal Analysis of Sympton Expression in Grapevines Affected by Esca

An analysis of symptom expression in esca infected grapevines was performed by focusing on the dynamics of each plant. A parametric statistical model was proposed to evaluate the probability that a plant would show esca symptoms at given values for a relevant set of factors (year, presence of symptoms in the previous year, presence of plants with symptoms in the close neighborhood). The statistical tests of the hypotheses revealed that the considered factors explained a large amount of the observed variability. In particular, the state of plants in the close vicinity is one of those factors. Thus we found evidence that there was an association between plant vicinity and esca symptoms. Future developments of our model will include the factors field column and weather

a conditional linear gaussian network to assess the impact of several agronomic settings on the quality of tuscan sangiovese grapes

Summary In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed

A Bayesian model for control strategy selection against Plasmopara viticola infections

Plant pathogens pose a persistent threat to grape production, causing significant economic losses if disease management strategies are not carefully planned and implemented. Simulation models are one approach to address this challenge because they provide short-term and field-scale disease prediction by incorporating the biological mechanisms of the disease process and the different phenological stages of the vines. In this study, we developed a Bayesian model to predict the probability of Plasmopara viticola infection in grapevines, considering various disease management approaches. To aid decision-making, we introduced a multi-attribute utility function that incorporated a sustainability index for each strategy. The data used in this study were derived from trials conducted during the production years 2018-2020, involving the application of five disease management strategies: conventional Integrated Pest Management (IPM), conventional organic, IPM with substantial fungicide reduction combined with host-defense inducing biostimulants, organic management with biostimulants, and the use of biostimulants only. Two scenarios were considered, one with medium pathogen pressure (Average) and another with high pathogen pressure (Severe). The results indicated that when sustainability indexes were not considered, the conventional IPM strategy provided the most effective disease management in the Average scenario. However, when sustainability indexes were included, the utility values of conventional strategies approached those of reduced fungicide strategies due to their lower environmental impact. In the Severe scenario, the application of biostimulants alone emerged as the most effective strategy. These results suggest that in situations of high disease pressure, the use of conventional strategies effectively combats the disease but at the expense of a greater environmental impact. In contrast to mechanistic-deterministic approaches recently published in the literature, the proposed Bayesian model takes into account the main sources of heterogeneity through the two group-level effects, providing accurate predictions, although precise estimates of random effects may require larger samples than usual. Moreover, the proposed Bayesian model assists the agronomist in selecting the most effective crop protection strategy while accounting for induced environmental side effects through customizable utility functions

P1245 Polymorphic Variants of HSD3B1 Gene Confer Different Outcome in Specific Subgroups of Patients Infected With SARS-CoV-2

Introduction: Severe respiratory syndrome coronavirus 2 (SARS-CoV-2) uses the androgen receptor (AR), through ACE2 receptor and TMPRSS2, to enter nasal and upper airways epithelial cells. Genetic analyses revealed that HSD3B1 P1245C polymorphic variant increases dihydrotestosterone production and upregulation of TMPRSS2 with respect to P1245A variant, thus possibly influencing SARS-CoV-2 infection. Our aim was to characterize the HSD3B1 polymorphism status and its potential association with clinical outcomes in hospitalized patients with COVID-19 in Southern Switzerland. Materials and Methods: The cohort included 400 patients hospitalized for COVID-19 during the first wave between February and May 2020 in two different hospitals of Canton Ticino. Genomic DNA was extracted from formalin-fixed paraffin-embedded tissue blocks, and HSD3B1 gene polymorphism was evaluated by Sanger sequencing. Statistical associations were verified using different test. Results: HSD3B1 polymorphic variants were not associated with a single classical factor related to worse clinical prognosis in hospitalized patients with SARS-CoV-2. However, in specific subgroups, HSD3B1 variants played a clinical role: intensive care unit admission was more probable in patients with P1245C diabetes compared with P1245A individuals without this comorbidity and death was more associated with hypertensive P1245A>C cases than patients with P1245A diabetes without hypertension. Discussion: This is the first study showing that HSD3B1 gene status may influence the severity of SARS-CoV-2 infection. If confirmed, our results could lead to the introduction of HSD3B1 gene status analysis in patients infected with SARS-CoV-2 to predict clinical outcome. Keywords: HSD3B1 gene polymorphism; Likelihood-ratio tests; SARS-CoV-2; androgen receptor; direct sequencing

A Bayesian Causal Model to Support Decisions on Treating of a Vineyard

Plasmopara viticola is one of the main challenges of working in a vineyard as it can seriously damage plants, reducing the quality and quantity of grapes. Statistical predictions on future incidence may be used to evaluate when and which treatments are required in order to define an efficient and environmentally friendly management. Approaches in the literature describe mechanistic models requiring challenging calibration in order to account for local features of the vineyard. A causal Directed Acyclic Graph is here proposed to relate key determinants of the spread of infection within rows of the vineyard characterized by their own microclimate. The identifiability of causal effects about new chemical treatments in a non-randomized regime is discussed, together with the context in which the proposed model is expected to support optimal decision-making. A Bayesian Network based on discretized random variables was coded after quantifying the expert degree of belief about features of the considered vineyard. The predictive distribution of incidence, given alternative treatment decisions, was defined and calculated using the elicited network to support decision-making on a weekly basis. The final discussion considers current limitations of the approach and some directions for future work, such as the introduction of variables to describe the state of soil and plants after treatment