Hydrogeochemical processes driving elevated Arsenic and Fluoride in the Texas Gulf Coast Aquifer - Hailey N. Goodwin; Taehyun Roh; Reid Buskirk; D. Kirk Nordstrom; Franco Marcantonio; Peter S.K. Knappett (Applied Geochemistry, 2026)

Linking conventional activated sludge treatment plant performances for micropollutants removal to environmental risk and SimpleTreat model assessment - C. Di Marcantonio; A. Chiavola; D. Spagnoli; B. Meşe; F. Margarita; V. Gioia; A. Frugis; S. Leoni; G. Cecchini; M. Spizzirri; M. R. Boni (International Journal of Environmental Science and Technology, 2024)
The attention to the contaminants of emerging concerns or micropollutants is continuously rising, also stimulated by the new initiatives at European level. The present study focuses on the removal of target micropollutants in a conventional activated sludge wastewater treatment plant which is the most common system and considered a source of micropollutants to the environment. The aims were to evaluate the removal efficiency of different stages of the wastewater treatment plant and to conduct the Environmental Risk Assessment on the final effluent. As indicated by the European’s guidelines on Environmental Risk Assessment, the SimpleTreat4.1 model was applied to this purpose: different values of the biodegradation constant, which represents the key factor for modelling, were applied, and the effects on the quality of prediction were evaluated. The evaluations were carried out thorough 3-year monitoring activity in a full-scale wastewater treatment plant on 10 micropollutants belonging to the classes of illicit drugs, pharmaceuticals and psychoactive. The model was found statistically not satisfactory for most of the investigated micropollutants. A better prediction was obtained when the biodegradation constant was calculated based on the full-scale data. Nonetheless, the results obtained in terms of risk were considered acceptable and comparable among each other. Graphical abstract

Detection and full genomic sequencing of rare hepatitis E virus genotype 4d in Italian wastewater, undetected by clinical surveillance - C. Veneri; D. Brandtner; P. Mancini; G. Bonanno Ferraro; M. Iaconelli; C. Del Giudice; A.R. Ciccaglione; R. Bruni; M. Equestre; C. Marcantonio; E. Suffredini; G. La Rosa (Science of The Total Environment, 2024)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

From zero to infinity: Minimum to maximum diversity of the planet by spatio‐parametric Rao’s quadratic entropy - Duccio Rocchini; Matteo Marcantonio; Daniele Da Re; Giovanni Bacaro; Enrico Feoli; Giles M. Foody; Reinhard Furrer; Ryan J. Harrigan; David Kleijn; Martina Iannacito; Jonathan Lenoir; Meixi Lin; et al. (Global Ecology and Biogeography, 2021)
Aim The majority of work done to gather information on the Earth's biodiversity has been carried out using in‐situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions The proposed unifying measure is an integration between abundance‐ and distance‐based algorithms to map the continuum of diversity given a satellite image at any spatial scale. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.

The role of PKCε-dependent signaling for cardiac differentiation - D. Galli; G. Gobbi; C. Carrubbi; D. Marcantonio; L. Benedetti; M. G. C. De Angelis; T. Meschi; M. Vaccarezza; M. Sampaolesi; P. Mirandola; M. Vitale (Histochemistry and Cell Biology, 2013)