Serena Arima

Use of network analysis to capture key traits affecting tomato organoleptic quality

The long-term objective of tomato breeders is to identify metabolites that contribute to defining the target flavour and to design strategies to enhance it. This paper reports the results of network analysis, based on metabolic phenotypic and sensory data, to highlight important relationships among such traits. This tool allowed a reduction in data set complexity, building a network consisting of 35 nodes and 74 links corresponding to the 74 significant (positive or negative) correlations among the variables studied. A number of links among traits contributing to fruit organoleptic quality and to the perception of sensory attributes were identified. Modular partitioning of the characteristics involved in fruit organoleptic perception captured the essential fruit parameters that regulate interactions among different class traits. The main feature of the network was the presence of three nodes interconnected among themselves (dry matter, pH, and °Brix) and with other traits, and nodes with widely different linkage degrees. Identification of strong associations between some metabolic and sensory traits, such as citric acid with tomato smell, glycine with tomato smell, and granulosity with dry matter, suggests a basis for more targeted investigations in the future.

Identification of Relevant Conformational Epitopes on the HER2 Oncoprotein by Using Large Fragment Phage Display (LFPD)

We developed a new phage-display based approach, the Large Fragment Phage Display (LFPD), that can be used for mapping conformational epitopes on target molecules of immunological interest. LFPD uses a simplified and more effective phage-display approach in which only a limited set of larger fragments (about 100 aa in length) are expressed on the phage surface. Using the human HER2 oncoprotein as a target, we identified novel B-cell conformational epitopes. The same homologous epitopes were also detected in rat HER2 and all corresponded to the epitopes predicted by computational analysis (PEPITO software), showing that LFPD gives reproducible and accurate results. Interestingly, these newly identified HER2 epitopes seem to be crucial for an effective immune response against HER2-overexpressing breast cancers and might help discriminating between metastatic breast cancer and early breast cancer patients. Overall, the results obtained in this study demonstrated the utility of LFPD and its potential application to the detection of conformational epitopes on many other molecules of interest, as well as, the development of new and potentially more effective B-cell conformational epitopes based vaccines.

Improved Harmonic Mean Estimator for Phylogenetic Model Evidence

Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systematics. Many phylogenetic models are often at stake, and different approaches are used to compare them within a Bayesian framework. The Bayes factor, defined as the ratio of the marginal likelihoods of two competing models, plays a key role in Bayesian model selection. We focus on an alternative estimator of the marginal likelihood whose computation is still a challenging problem. Several computational solutions have been proposed, none of which can be considered outperforming the others simultaneously in terms of simplicity of implementation, computational burden and precision of the estimates. Practitioners and researchers, often led by available software, have privileged so far the simplicity of the harmonic mean (HM) estimator. However, it is known that the resulting estimates of the Bayesian evidence in favor of one model are biased and often inaccurate, up to having an infinite variance so that the reliability of the corresponding conclusions is doubtful. We consider possible improvements of the generalized harmonic mean (GHM) idea that recycle Markov Chain Monte Carlo (MCMC) simulations from the posterior, share the computational simplicity of the original HM estimator, but, unlike it, overcome the infinite variance issue. We show reliability and comparative performance of the improved harmonic mean estimators comparing them to approximation techniques relying on improved variants of the thermodynamic integration.

Respiratory syncytial virus bronchiolitis, weather conditions and air pollution in an Italian urban area: An observational study

Background In this study we sought to evaluate the association between viral bronchiolitis, weather conditions, and air pollution in an urban area in Italy. Methods We included infants hospitalized for acute bronchiolitis from 2004 to 2014. All infants underwent a nasal washing for virus detection. A regional agency network collected meteorological data (mean temperature, relative humidity and wind velocity) and the following air pollutants: sulfur dioxide, nitrogen oxide, carbon monoxide, ozone, benzene and suspended particulate matter measuring less than 10 &mgr;m (PM10) and less than 2.5 &mgr;m (PM2.5) in aerodynamic diameter. We obtained mean weekly concentration data for the day of admission, from the urban background monitoring sites nearest to each childs home address. Overdispersed Poisson regression model was fitted and adjusted for seasonality of the respiratory syncytial virus (RSV) infection, to evaluate the impact of individual characteristics and environmental factors on the probability of a being positive RSV. Results Of the 723 nasal washings from the infants enrolled, 266 (68%) contained RSV, 63 (16.1%) rhinovirus, 26 (6.6%) human bocavirus, 20 (5.1%) human metapneumovirus, and 16 (2.2%) other viruses. The number of RSV‐positive infants correlated negatively with temperature (p < 0.001), and positively with relative humidity (p < 0.001). Air pollutant concentrations differed significantly during the peak RSV months and the other months. Benzene concentration was independently associated with RSV incidence (p = 0.0124). Conclusions Seasonal weather conditions and concentration of air pollutants seem to influence RSV‐related bronchiolitis epidemics in an Italian urban area. HighlightsPeak RSV activity correlates with cold temperatures and higher relative humidity.RSV‐positive cases correlates positively with BZ, NOx, SO2, PM10 and PM2.5.The most predictive pollutant for RSV cases (constant temperature and humidity)is BZ.

Bayesian analysis of three indices for lagoons ecological status evaluation

The Water Framework Directive (WFD) recognizes benthic macroinvertebrates as a good biological quality element for transitional waters as they are the most exposed to natural variability patterns characteristic of these ecosystems, due to their life cycles and space-use behavior. In this paper we consider the performance of three multimetric indices (namely M-AMBI, BITS and ISS) based on benthic macroinvertebrates abundances, aiming at assessing the ecological status of lagoons and likely to respond differently to different sources of stress and natural variability. In order to investigate the possible contrasting behavior of the three multimetric indices, we propose a Bayesian hierarchical model in which they are jointly modeled as functions of abiotic covariates, external anthropogenic pressure indicators and lagoon effects. The proposed model is applied to data from three lagoons in Apulia and assessed using multiple diagnostic tools. The joint sensitivity of lagoon quality evaluations to available covariates is thus investigated.

Early invasive versus early conservative strategy in non-ST-elevation acute coronary syndrome: An outcome research study

Background: An early invasive strategy (EIS) has been shown to yield a better clinical outcome than an early conservative strategy (ECS) in patients with non-ST-elevation acute coronary syndromes (NSTEACSs), particularly in those at higher risk according to the GRACE risk score. However, findings of the clinical trials have not been confirmed in registries. Objective: To investigate the outcome of patients with NSTEACS treated according to an EIS or a ECS in a real-world all-comers outcome research study. Methods: The primary hypothesis of the study was the non-inferiority of an ECS in comparison with an EIS as to a combined primary end-point of death, non-fatal myocardial infarction and hospital readmission for acute coronary syndromes at one year. Participating centres were divided into two groups: those with a pre-specified routine EIS and those with a pre-specified routine ECS. Two statistical analyses were performed: a) an ‘intention to treat’ analysis: all patients were considered to be treated according to the pre-specified routine strategy of that centre; b) a ‘per protocol’ analysis: patients were analysed according to the actual treatment applied. Cox model including propensity score correction was applied for all analyses. Results: The intention to treat analysis showed an equivalence between EIS and ECS (11.4% vs. 11.1%) with regard to the primary end-point incidence at one year. In the three subgroups of patients according to the GRACE risk score (⩽ 108, 109–140, > 140), EIS and ECS confirmed their equivalence (5.3% vs. 3.9%, 8.4% vs. 7.6%, and 20.3% vs. 20.9%, respectively). When the per protocol analysis was applied, a reduction of the primary end-point at one year with EIS vs. ECS was demonstrated (6.2% vs. 15.3%, p=0.021); analysis of the subgroups according to the GRACE risk score numerically confirmed these data (3.1% vs. 6.5%, 5.1% vs. 10.0%, and 10.8% vs. 24.5%, respectively). Conclusions: In a real-life registry of all-comers NSTEACS patients, ECS was non-inferior to EIS; however, when EIS was applied according to clinical judgement, a reduction of clinical events at one year was demonstrated.

La condivisione dei lavori domestici tra uomini e donne. Uno studio sui dati italiani dell’uso del tempo

Among European countries, Italian couples show one of the widest gender gaps in housework division: Italian women still carry out three-quarters of family labour. Following the existing literature, this article focuses on three theoretical explanations of the persistence of the gendered division of unpaid work: time availability, relative resources, and conformity to traditional gender ideology. Time-Use data from the 2008/2009 Survey edition has been used to study the behaviour of Italian couples, married or living together, where the women are employed. The amount of time spent by men and women in domestic tasks has been modelled as function of several family characteristics and a Tobit model has been used in order to take into account the truncated nature of the dependent variables. Results show that the amount of time dedicated by women to housework significantly decreases when they take on the role of breadwinner, whereas the involvement in domestic tasks of male partners increases when they are unemployed. Therefore, in conformity with the expectations of relative resources’ hypothesis, Italian data shows a positive impact of the female financial capacity in reducing gender segregation in housework.

A Bayesian hierarchical model for identifying epitopes in peptide microarray data

Peptide Microarray Immunoassay (PMI for brevity) is a novel technology that enables researchers to map a large number of proteomic measurements at a peptide level, providing information regarding the relationship between antibody response and clinical sensitivity. PMI studies aim at recognizing antigen-specific antibodies from serum samples and at detecting epitope regions of the protein antigen. PMI data present new challenges for statistical analysis mainly due to the structural dependence among peptides. A PMI is made of a complete library of consecutive peptides. They are synthesized by systematically shifting a window of a fixed number of amino acids through the finite sequence of amino acids of the antigen protein as ordered in the primary structure of the protein. This implies that consecutive peptides have a certain number of amino acids in common and hence are structurally dependent. We propose a new flexible Bayesian hierarchical model framework, which allows one to detect recognized peptides and bound epitope regions in a single framework, taking into account the structural dependence between peptides through a suitable latent Markov structure. The proposed model is illustrated using PMI data from a recent study about egg allergy. A simulation study shows that the proposed model is more powerful and robust in terms of epitope detection than simpler models overlooking some of the dependence structure.

Exploiting blank spots for model-based background correction in discovering genes with DNA array data:

Motivated by a real data set deriving from a study on the genetic determinants of the behavior of Mycobacterium tuberculosis (MTB) hosted in macrophage, we take advantage of the presence of control spots and illustrate modelling issues for background correction and the ensuing empirical findings resulting from a Bayesian hierarchical approach to the problem of detecting differentially expressed genes. We prove the usefulness of a fully integrated approach where background correction and normalization are embedded in a single model-based framework, creating a new tailored model to account for the peculiar features of DNA array data where null expressions are planned by design. We also advocate the use of an alternative normalization device resulting from a suitable reparameterization. The new model is validated by using both simulated and our MTB data. This work suggests that the presence of a substantial fraction of exact null expressions might be the effect of an imperfect background calibration and shows how this can be suitably re-calibrated with the information coming from control spots. The proposed idea can be extended to all experiments in which a subset of genes whose expression levels can be ascribed mainly to background noise is planned by design.

Modifiable risk factors associated with bronchiolitis

Background: We sought to clarify possibly modifiable risk factors related to pollution responsible for acute bronchiolitis in hospitalized infants. Methods: For this observational study, we recruited 213 consecutive infants with bronchiolitis (cases: median age: 2 months; age range: 0.5–12 months; boys: 55.4%) and 213 children aged <3 years (controls: median age: 12 months; age range: 0.5–36 months; boys: 54.5%) with a negative medical history for lower respiratory tract diseases hospitalized at ‘Sapienza’ University Rome and IRCCS Bambino Gesù Hospital. Infants’ parents completed a standardized 53-item questionnaire seeking information on social-demographic and clinical characteristics, indoor pollution, eating habits and outdoor air pollution. Multivariate logistic regression analyses were run to assess the independent effect of risk factors, accounting for confounders and effect modifiers. Results: In the 213 hospitalized infants the questionnaire identified the following risk factors for acute bronchiolitis: breastfeeding ⩾3 months (OR: 2.1, 95% confidence interval [CI]: 1.2–3.6), presence of older siblings (OR: 2.8, 95% CI: 1.7–4.7), ⩾4 cohabitants (OR: 1.5, 95% CI: 1.1–2.1), and using seed oil for cooking (OR: 1.7, 95% CI: 1.2–2.6). Having renovated their home in the past 12 months and concurrently being exposed daily to smoking, involving more than 11 cigarettes and two or more smoking cohabitants, were more frequent factors in cases than in controls (p = 0.021 and 0.05), whereas self-estimated proximity to road and traffic was similar in the two groups. Conclusions: We identified several risk factors for acute bronchiolitis related to indoor and outdoor pollution, including inhaling cooking oil fumes. Having this information would help public health authorities draw up effective preventive measures – for example, teach mothers to avoid handling their child when they have a cold and eliminate exposure to second-hand tobacco smoke.