Building the European Alien Species Information Network (EASIN): a novel approach for the exploration of distributed alien species data

The European Alien Species Information Network (EASIN; http://easin.jrc.ec.europa.eu) aims to facilitate the exploration of existing alien species information from distributed sources through a network of interoperable web services, and to assist the implementation of European policies on biological invasions. The network allows extraction of alien species information from online information systems for all species included in the EASIN catalogue. This catalogue was based on an inventory of reported alien species in Europe that was produced by reviewing and standardizing information from 43 online databases. It includes information on taxonomy, synonyms, common names, pathways of introduction, native range in Europe, and impact. EASIN catalogue entails the basic information needed to efficiently link to existing online databases and retrieve spatial information for alien species distribution in Europe. Using search functionality powered by a widget framework, it is possible to make a tailored selection of a subgroup of species based on various criteria (e.g., environment, taxonomy, pathways). Distribution maps of the selected species can be produced dynamically and downloaded by the user. The EASIN web tools and services follow internationally recognized standards and protocols, and can be utilized freely and independently by any website, while ownership of the data remains with its source, which is properly cited and linked.JRC.H.1-Water Resource

Is it possible to compare inhibitory and excitatory intracortical circuits in face and hand primary motor cortex?

Face muscles are important in a variety of different functions, such as feeding, speech and communication of non-verbal affective states, which require quite different patterns of activity from those of a typical hand muscle. We ask whether there are differences in their neurophysiological control that might reflect this. Fifteen healthy individuals were studied. Standard single- and paired-pulse transcranial magnetic stimulation (TMS) methods were used to compare intracortical inhibitory (short interval intracortical inhibition (SICI); cortical silent period (CSP)) and excitatory circuitries (short interval intracortical facilitation (SICF)) in two typical muscles, the depressor anguli oris (DAO), a face muscle, and the first dorsal interosseous (FDI), a hand muscle. TMS threshold was higher in DAO than in FDI. Over a range of intensities, resting SICF was not different between DAO and FDI, while during muscle activation SICF was stronger in FDI than in DAO (P = 0.012). At rest, SICI was stronger in FDI than in DAO (P = 0.038) but during muscle contraction, SICI was weaker in FDI than in DAO (P = 0.034). We argue that although many of the difference in response to the TMS protocols could result from the difference in thresholds, some, such as the reduction of resting SICI in DAO, may reflect fundamental differences in the physiology of the two muscle groups

How Covid Mobility Restrictions Modified the Population of Investors in Italian Stock Markets // L’evoluzione della composizione del retail trading sul mercato azionario italiano a seguito delle restrizioni imposte dalla pandemia da Covid

This paper investigates how Covid mobility restrictions impacted the population of investors of the Italian stock market. The analysis tracks the trading activity of individual investors in Italian stocks in the period January 2019-September 2021, investigating how their composition and the trading activity changed around the Covid-19 lockdown period (March 9 - May 19, 2020) and more generally in the period of the pandemic. The results pinpoint that the lockdown restriction was accompanied by a surge in interest toward stock market, as testified by the trading volume by households. Given the generically falling prices during the lockdown, the households, which are typically contrarian, were net buyers, even if less than expected from their trading activity in 2019. This can be explained by the arrival, during the lockdown, of a group of ∼ 185k new investors (i.e. which had never traded since January 2019) which were on average ten year younger and with a larger fraction of males than the pre-lockdown investors. By looking at the gross P&L, there is clear evidence that these new investors were more skilled in trading. There are thus indications that the lockdown, and more generally the Covid pandemic, created a sort of regime change in the population of financial investors

A machine learning approach to support decision in insider trading detection

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to her own past trading history and on the present trading activity of her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids

A machine learning approach to support decision in insider trading detection

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.Comment: 42 pages, 16 Figure

A machine learning approach to support decision in insider trading detection

Identifying market abuse activity from data on investors’ trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to her own past trading history and on the present trading activity of her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids

Evaluation of Legionella Air Contamination in Healthcare Facilities by Different Sampling Methods: An Italian Multicenter Study

Healthcare facilities (HF) represent an at-risk environment for legionellosis transmission occurring after inhalation of contaminated aerosols. In general, the control of water is preferred to that of air because, to date, there are no standardized sampling protocols. Legionella air contamination was investigated in the bathrooms of 11 HF by active sampling (Surface Air System and Coriolis®μ) and passive sampling using settling plates. During the 8-hour sampling, hot tap water was sampled three times. All air samples were evaluated using culture-based methods, whereas liquid samples collected using the Coriolis®μ were also analyzed by real-time PCR. Legionella presence in the air and water was then compared by sequence-based typing (SBT) methods. Air contamination was found in four HF (36.4%) by at least one of the culturable methods. The culturable investigation by Coriolis®μ did not yield Legionella in any enrolled HF. However, molecular investigation using Coriolis®μ resulted in eight HF testing positive for Legionella in the air. Comparison of Legionella air and water contamination indicated that Legionella water concentration could be predictive of its presence in the air. Furthermore, a molecular study of 12 L. pneumophila strains confirmed a match between the Legionella strains from air and water samples by SBT for three out of four HF that tested positive for Legionella by at least one of the culturable methods. Overall, our study shows that Legionella air detection cannot replace water sampling because the absence of microorganisms from the air does not necessarily represent their absence from water; nevertheless, air sampling may provide useful information for risk assessment. The liquid impingement technique appears to have the greatest capacity for collecting airborne Legionella if combined with molecular investigation

MaCGE-MOD: Malta's Computable General Equilibrium Model

This paper introduces the latest addition to the modelling toolkit of the Central Bank of Malta: a static Computable General Equilibrium model for Malta named MaCGE-MOD. Developed through a collaboration with the University of Macerata, the model is a multi-input, multi-output and multi-sector model, calibrated on a Social Accounting Matrix which illustrates the income flows among production processes and institutional sectors in Malta. The model constitutes a set of equations which describe the circular flow of income in the economy and the behaviour of economic agents. It is a useful tool to determine ex ante the impacts of policy decisions or exogenous shocks once all the associated direct, indirect and induced effects have propagated through the economy. As it is based on a Social Accounting Matrix, the model can provide disaggregated sectoral analyses to complement analyses of the aggregate effects of policy-making scenarios or exogenous shocks. Thus, MaCGE-MOD can potentially be an important tool for policy makers to identify effects not easily discernible from the other aggregated models currently available

Clinical global impression-severity score as a reliable measure for routine evaluation of remission in schizophrenia and schizoaffective disorders

Aims: This study aimed to compare the performance of Positive and Negative Syndrome Scale (PANSS) symptom severity criteria established by the Remission in Schizophrenia Working Group (RSWG) with criteria based on Clinical Global Impression (CGI) severity score. The 6-month duration criterion was not taken into consideration. Methods: A convenience sample of 112 chronic psychotic outpatients was examined. Symptomatic remission was evaluated according to RSWG severity criterion and to a severity criterion indicated by the overall score obtained at CGI-Schizophrenia (CGI-SCH) rating scale (≤3) (CGI-S). Results: Clinical remission rates of 50% and 49.1%, respectively, were given by RSWG and CGI-S, with a significant level of agreement between the two criteria in identifying remitted and non-remitted cases. Mean scores at CGI-SCH and PANSS scales were significantly higher among remitters, independent of the remission criteria adopted. Measures of cognitive functioning were largely independent of clinical remission evaluated according to both RSWG and CGI-S. When applying RSWG and CGI-S criteria, the rates of overall good functioning yielded by Personal and Social Performance scale (PSP) were 32.1% and 32.7%, respectively, while the mean scores at PSP scale differed significantly between remitted and non-remitted patients, independent of criteria adopted. The proportion of patients judged to be in a state of well-being on Social Well-Being Under Neuroleptics-Short Version scale (SWN-K) were, respectively, 66.1% and 74.5% among remitters according to RSWG and CGI-S; the mean scores at the SWN scale were significantly higher only among remitters according to CGI-S criteria. Conclusions: CGI severity criteria may represent a valid and user-friendly alternative for use in identifying patients in remission, particularly in routine clinical practic

Transcutaneous trigeminal nerve stimulation modulates the hand blink reflex

The hand-blink reflex (HBR) is a subcortical response, elicited by the electrical stimulation of the median nerve, whose magnitude is specifically modulated according to the spatial properties of the defensive peripersonal space (DPPS) of the face. For these reasons, the HBR is commonly used as a model to assess the DPPS of the face. Little is known on the effects induced by the activation of cutaneous afferents from the face on the DPPS of the face. Therefore, we tested the effect of non-painful transcutaneous trigeminal nerve stimulation (TNS) on the amplitude of the HBR. Fifteen healthy participants underwent HBR recording before and after 20 min of sham- and real- TNS delivered bilaterally to the infraorbital nerve in two separate sessions. The HBR was recorded bilaterally from the orbicularis oculi muscles, following non-painful median nerve stimulation at the wrist. The HBR amplitude was assessed in the “hand‐far” and “hand‐near” conditions, relative to the hand position in respect to the face. The amplitudes of the hand-far and hand-near HBR were measured bilaterally before and after sham- and real-TNS. Real-TNS significantly reduced the magnitude of the HBR, while sham-TNS had no significant effect. The inhibitory effect of TNS was of similar extent on both the hand-far and hand-near components of the HBR, which suggests an action exerted mainly at brainstem level