This study explores the circular economy (CE) practices of Small and Medium Enterprises (SMEs) in the 28 European Union (EU) member states. Five measures of CE are studied, namely Re-planning the way water is used to minimize usage and maximize re-usage, Using renewable energy, Replanning energy usage to minimize consumption, Minimizing waste by recycling or reusing waste or selling it to another firm, and Redesigning products and services to minimize the use of materials or using recycled materials. Multilevel ordinal probit models that control within-and betweenvariability across European Union countries are estimated. Results show that CE measures across EU countries are very heterogeneous. At the firm level, we find that firm size (number of employees and total turnover in 2015) and percentage of firms' turnover invested in R&D in 2015 are significant in explaining within-country variations. The multilevel structure (between-country variability) accounts for 6.1% to 15.1% of the total variability of CE measures. These results have implications for the design of framework policies at EU level given that the firms surveyed are SMEs, the segment in which these CE measures most need improved planning and implementation.

This paper focuses on the implementation of circular economy (CE) practices in small‐ and medium‐sized firms in all 28 European Union (EU) countries. The analyses take into account the hierarchical nature of the collected data as firms are nested within EU countries, that is, the heterogeneity between different types of firms and countries according to practices and attitudes towards CE. The multilevel latent class model identifies groups of firms and groups of EU countries that are homogeneous in terms of CE, that is, how the homogeneous groups of small‐ and medium‐sized enterprises (SMEs) are distributed across the groups of EU countries. These results, together with the fact that firms with similar CE attitudes and practices have different demographic and business profiles across groups of countries, shed further light on the topic of green behavior in the EU with implications for businesses' environmental policies. Moreover, indications emerge that European policies favoring the implementation of CE practices should be targeted at least for subgroups of European countries, considering the different composition by typology of SMEs operating in their territories and that, at the same time, policies should be defined within each group of countries to account for the specific features of each of the four classes of SMEs.

BackgroundCats are susceptible to feline panleukopenia virus (FPV) and canine parvovirus (CPV) variants 2a, 2b and 2c. Detection of FPV and CPV variants in apparently healthy cats and their persistence in white blood cells (WBC) and other tissues when neutralising antibodies are simultaneously present, suggest that parvovirus may persist long-term in the tissues of cats post-infection without causing clinical signs. The aim of this study was to screen a population of 54 cats from Sardinia (Italy) for the presence of both FPV and CPV DNA within buffy coat samples using polymerase chain reaction (PCR). The DNA viral load, genetic diversity, phylogeny and antibody titres against parvoviruses were investigated in the positive cats.ResultsCarnivore protoparvovirus 1 DNA was detected in nine cats (16.7%). Viral DNA was reassembled to FPV in four cats and to CPV (CPV-2b and 2c) in four cats; one subject showed an unusually high genetic complexity with mixed infection involving FPV and CPV-2c. Antibodies against parvovirus were detected in all subjects which tested positive to DNA parvoviruses.ConclusionsThe identification of FPV and CPV DNA in the WBC of asymptomatic cats, despite the presence of specific antibodies against parvoviruses, and the high genetic heterogeneity detected in one sample, confirmed the relevant epidemiological role of cats in parvovirus infection.

Cortisol, androstenedione, testosterone, dehydroepiandrosterone sulphate (DHAS) and free dehydroepiandrosterone (DHA) were measured in plasma of ten women affected by amenorrhoea with hyperprolactinaemia and eleven women affected by secondary hypothalamic amenorrhoea; twelve normal women at the second day of the menstrual cycle were used as controls. All subjects were hospitalized and 17-ketosteroids, 17OH-corticosteroids and total dehydroepiandrosterone were also measured in urine. Plasma DHAS was increased in all subjects affected by amenorrhoea with hyperprolactinaemia, while plasma DHA and urinary DHA were significantly increased in this group in comparison to other groups. Plasma cortisol, androstenedione and testosterone and urinary 17-oxosteroids and 17OH-corticosteroids were not significantly differnt in the three groups. In subjects affected by amenorrhoea with hyperprolactinaemia treated with bromocriptine a clear decrease of DHAS correlating with a decrease of plasma prolactin was observed. Since in wome DHAS sems to be almost exclusively secreted by the adrenal gland and most of the circulating DHA is dervied from adrenal secretion, these data suggest that human prolactin can stimulate DHAS production by the adrenal cortex.

Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of gross change, whereas systematic, correlated errors usually cause underestimation of the transitions. Furthermore, the patterns of true change may be seriously distorted by independent or systematic classification errors. Latent class models and directed log-linear analysis are excellent tools to correct for both independent and correlated measurement errors. An extensive example on labor market states taken from the Survey of Income and Program Participation panel is presented.