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The European Biological Variation Study (EuBIVAS), which includes 91 healthy volunteers from five European countries, estimated high-quality biological variation (BV) data for several measurands. Previous EuBIVAS papers reported no significant differences among laboratories/population; however, they were focused on specific set of measurands, without a comprehensive general look. The aim of this paper is to evaluate the homogeneity of EuBIVAS data considering multivariate information applying the Principal Component Analysis (PCA), a machine learning unsupervised algorithm. The EuBIVAS data for 13 basic metabolic panel linked measurands (glucose, albumin, total protein, electrolytes, urea, total bilirubin, creatinine, phosphatase alkaline, aminotransferases), age, sex, menopause, body mass index (BMI), country, alcohol, smoking habits, and physical activity, have been used to generate three databases developed using the traditional univariate and the multivariate Elliptic Envelope approaches to detect outliers, and different missing-value imputations. Two matrix of data for each database, reporting both mean values, and "within-person BV"(CVP) values for any measurand/subject, were analyzed using PCA. A clear clustering between males and females mean values has been identified, where the menopausal females are closer to the males. Data interpretations for the three databases are similar. No significant differences for both mean and CVPs values, for countries, alcohol, smoking habits, BMI and physical activity, have been found. The absence of meaningful differences among countries confirms the EuBIVAS sample homogeneity and that the obtained data are widely applicable to deliver APS. Our data suggest that the use of PCA and the multivariate approach may be used to detect outliers, although further studies are required.

The rapid detection of SARS-CoV-2 infections is essential for both diagnostic and prognostic reasons: However, the current gold standard for COVID-19 diagnosis, that is the rRT-PCR test, is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Thus, Machine Learning (ML) based methods have recently attracted increasing interest, especially when applied to digital imaging (x-rays and CT scans). In this article, we review the literature on ML-based diagnostic and prognostic methods grounding on hematochemical parameters. In doing so, we address the gap in the existing literature, which has so far neglected the application of ML to laboratory medicine. We surveyed 20 research articles, extracted from the Scopus and PubMed indexes. These studies were characterized by a large heterogeneity, in terms of considered laboratory and clinical parameters, sample size, reference population, employed ML methods and validation procedures. Most studies were found to be affected by reporting and replicability issues: Among the surveyed studies, only three reported complete information regarding the analytic methods (units of measure, analyzing equipment), while nine studies reported no information at all. Furthermore, only six studies reported results on independent external validation. In light of these results, we discuss the importance of a tighter collaboration between data scientists and medicine laboratory professionals, so as to correctly characterize the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the correct interpretation of the results, and gain actual usefulness by applying ML methods in clinical practice.

Invasive alien plants are a major threat to biodiversity and they contribute to the unfavourable conservation status of habitats of interest to the European Community. In order to favour implementation of European Union Regulation no. 1143/2014 on invasive alien species, the Italian Society of Vegetation Science carried out a large survey led by a task force of 49 contributors with expertise in vegetation across all the Italian administrative regions. The survey summed up the knowledge on impact mechanisms of invasive alien plants in Italy and their outcomes on plant communities and the EU habitats of Community Interest, in accordance with Directive no. 92/43/EEC. The survey covered 241 alien plant species reported as having deleterious ecological impacts. The data collected illustrate the current state of the art, highlight the main gaps in knowledge, and suggest topics to be further investigated. In particular, the survey underlined competition as being the main mechanism of ecological impact on plant communities and Natura 2000 habitats. Of the 241 species, only Ailanthus altissima was found to exert an ecological impact on plant communities and Natura 2000 habitats in all Italian regions; while a further 20 species impact up to ten out of the 20 Italian administrative regions. Our data indicate that 84 out of 132 Natura 2000 Habitats (64%) are subjected to some degree of impact by invasive alien plants. Freshwater habitats and natural and semi-natural grassland formations were impacted by the highest number of alien species, followed by coastal sand dunes and inland dunes, and forests. Although not exhaustive, this research is the first example of nationwide evaluation of the ecological impacts of invasive alien plants on plant communities and Natura 2000 Habitats.

Prompt gamma rays from the spontaneous fission of Cf-252 were measured with cerium-doped LaBr3 detectors. The average prompt fission gamma-ray multiplicity, M-gamma, was determined as a function of fragment mass number (A) and total kinetic energy (TKE). High-statistics data, obtained from three detectors of different size at different angles relative to the fission chamber, confirms unequivocally a saw-tooth like shape of the gamma multiplicity as a function of fragment mass, in contrast to previously published data. In addition, the TKE-dependence of M-gamma was determined experimentally and compared with recent data., QC 20210728

Background: The sharing of excitation energy between the fission fragments is one of the key issues in studying nuclear fission. One way to address this is by studying prompt-fission neutron multiplicities as a function of other fission observables such as the mass, (nu) over bar (A). These are vital benchmark data for both fission and nuclear deexcitation models, putting constrains on the fragment excitation energy and hence on the competing prompt neutron/gamma-ray emission. Despite numerous detailed studies, recent measurements done at JRC-Geel with the SCINTIA array in the epithermal region show surprisingly strong discrepancies to earlier thermal fission data and the Wahl systematics. Purpose: The purpose was to perform measurements of the prompt-fission neutron multiplicity, as a function of fragment mass and total kinetic energy (TKE), in U-235(n(th), f) and Cf-252(sf), to verify and extend the SCINTIA results. Another goal was to validate the analysis methods, and prepare for planned investigations at excitation energies up to 5.5 MeV. Methods: The experiments were conducted at the former 7 MV Van de Graaff facility in JRC-Geel, using a Twin Frisch-Grid Ionization Chamber and two liquid scintillation detectors. A neutron beam with an average energy of 0.5 MeV was produced via the Li-7(p,n) reaction. The neutrons were thermalized by a 12 cm thick block of paraffin. Digital data acquisition systems were utilized. Comprehensive simulations were performed to verify the methodology and to investigate the role of the mass and energy resolution on measured (nu) over bar (A) and (nu) over bar (TKE) values. The simulation results also revealed that the partial derivative(nu) over bar/partial derivative A and partial derivative(TKE) over bar/partial derivative(nu) over bar are affected by the mass and energy resolution. However, the effect is small for the estimated resolutions of this work. Detailed Fluka simulations were performed to calculate the fraction of therma, QC 20210122

This study provides a first step toward the knowledge of the alien-dominated and co-dominated plant communities present in Italy. The first ever checklist of the alien phytocoenoses described or reported in literature for the Italian territory has been compiled, produced by data-mining in national and local thematic literature. The resulting vegetation-type draft-list has been checked in the light of the most recent syntaxonomic documentation and updated with regards to syntaxonomy and nomenclature, with special reference to the frame proposed in the Italian Vegetation Prodrome. The list includes 27 vascular and one bryophyte vegetation classes, hosting 194 low rank alien-dominated syntaxa . The different vegetation types detected for each syntaxonomic class and macro-vegetation group, defined by physiognomical and ecological attributes, are discussed.