Equine periodontal disease (EPD) is a painful oral inflammatory syndrome characterized by multifactorial pathogenesis. Although it is well known that bacterial proliferation and consequent gingivitis are caused by the decomposition process of food residues, in hypsodont species, the pathogenetic role and the different bacterial species involved in the progression of EPD must be fully clarified. This study aimed to investigate the association of bacteria, including the complex red bacteria (RCB), with EPD, and to evaluate possible EPD risk factors. Bacterial species, including Treponema denticola, Tannerella forsythia, Porphyromonas gingivalis (belonging to the RCB), Fusobacterium nucleatum, Veilonella parvula, and Prevotella intermedia, were investigated in 125 oral swabs from healthy and EPD-affected horses using real-time multiplex PCR. Subsequently, possible risk factors (i.e., age, gender, and breed of the animals and type of feed used) were evaluated using univariate and multivariate analyses. Tannerella spp. and Treponema spp. were detected in a significantly higher proportion of horses affected by EPD than in healthy animals, although pathogens belonging to RCB were detected in low number of horses. At the same time, none of the investigated pathogens was significantly associated with a particular stage of disease severity. Horses aged older than 20 years were at higher risk of EPD. The high rate of coinfection, statistically associated with EPD, supports the hypothesis that EPD is a complex syndrome characterized by the possible simultaneous involvement of several pathogens and an increased risk depending on the animal’s age. Constant oral hygiene is the best prevention to prevent and treat the disease, especially in old animals.

Under the influence of the health emergency triggered by the COVID-19 pandemic, many brands changed their communication strategy and included more or less explicit references to the principles of solidarity and fraternity in their TV commercials to boost the confidence and hope of Italian families during the lockdown. The traditional attitudes of the advertising format, which focused on product characteristics, were relegated to the background in order to reinforce the “brand image” through words, signs, hashtags and music that spread empathetic messages to all those who needed to regain hope and trust in a time of extreme emotional fragility. The objective of this paper is to identify the emotions and brand awareness during the lockdown using text mining techniques by measuring customer sentiment expressed on the Twitter social network. Our proposal starts from an unstructured corpus of 20,982 tweets processed with text data mining techniques to identify patterns and trends in people’s posts related to specific hashtags and TV ads produced during the COVID-19 pandemic. The innovations in the brand’s advertising among consumers seem to have triggered some sense of appreciation and gratitude, as well as a strong sense of belonging that was not present before, as the TV ads were perceived as a disruptive element in consumers’ tweets. Although this effect is clearly documented, in this paper we demonstrate its transitory nature, in the sense that the frequency of occurrence of terms associated with an emotional dimension peaks during the weeks of lockdown, and then gradually decreases.

In this paper, well known disnormality or kurtosis indexes are decomposed into contributions to the measure of disnormality of the center, sides and tails of distributions. The results of this study confirm that the above indexes measure mainly the length and thickness of distribution tails at the expense of the distribution center, which may be more or less peaked or flat. The nature of this decomposition suggests the adoption of new and more appropriate measures of disnormality to capture the various forms in which disnormality occurs. The purpose is to put forward new indexes which are able to measure the aspects of disnormality more appropriately. The proposed indexes are less biased when compared to the structure of well-known indexes and are able to value not only the tail characteristics of distributions, but also those of the sides and the center.

In this paper, we present a novel approach to customer satisfaction analysis of airport services based on the analysis of distributional data for constructing a bivariate performance indicator. Distributional data was introduced for describing macro-data coming from the aggregation of micro-data observed at the individual level. We use them to represent the distribution of the ratings given by 165 classes (macro-units) of airport customers for twelve observed aspects. We describe the trend of passenger satisfaction over time by extracting 165 macro units from a survey conducted among 13,047 passengers at Bari and Brindisi airports during the peak and off-peak seasons of 2015, 2016 and 2017. To obtain a performance indicator, we performed a multiple factor analysis for distributional data. To our knowledge, no other methods exist for the factor analysis of multiple distributional variables. Further, we propose a new visualization tool called Green Eye Iris plot, which allows a joint visualization of our set of distributional values. The obtained results show that the distributional data analysis approach can provide valuable information at macro level that could be hidden when analyzing micro-data or when macro data are represented only by some features coming from summary statistics of groups.