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Background Pollution has been suggested as a precipitating factor for cardiovascular diseases. However, data about the link between air pollution and the risk of out-of-hospital cardiac arrest (OHCA) are limited and controversial. Methods By collecting data both in the OHCA registry and in the database of the regional agency for environmental protection (ARPA) of the Lombardy region, all medical OHCAs and the mean daily concentration of pollutants including fine particulate matter (PM10, PM2.5), benzene (C6H6), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) were considered from January 1st to December 31st, 2019 in the southern part of the Lombardy region (provinces of Pavia, Lodi, Cremona and Mantua; 7863 km2; about 1550000 inhabitants). Days were divided into high or low incidence of OHCA according to the median value. A Probit dose-response analysis and both uni- and multivariable logistic regression models were provided for each pollutant. Results The concentrations of all the pollutants were significantly higher in days with high incidence of OHCA except for O3, which showed a significant countertrend. After correcting for temperature, a significant dose-response relationship was demonstrated for all the pollutants examined. All the pollutants were also strongly associated with high incidence of OHCA in multivariable analysis with correction for temperature, humidity, and day-to-day concentration changes. Conclusions Our results clarify the link between pollutants and the acute risk of cardiac arrest suggesting the need of both improving the air quality and integrating pollution data in future models for the organization of emergency medical services.

Aim: To identify factors that may predict and affect the risk of relapse in anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. Method: This was a retrospective study of an Italian cohort of patients with paediatric (≤18y) onset anti-NMDAR encephalitis. Results: Of the 62 children included (39 females; median age at onset 9y 10mo, range 1y 2mo–18y; onset between 2005 and 2018), 21 per cent relapsed (median two total events per relapsing patient, range 2–4). Time to first relapse was median 31.5months (range 7–89mo). Severity at first relapse was lower than onset (median modified Rankin Scale [mRS] 3, range 2–4, vs median mRS 5, range 3–5; admission to intensive care unit: 0/10 vs 3/10). At the survival analysis, the risk of relapsing was significantly lower in patients who received three or more different immune therapies at first disease event (hazard ratio 0.208, 95% confidence interval 0.046–0.941; p=0.042). Neurological outcome at follow-up did not differ significantly between patients with relapsing and monophasic disease (mRS 0–1 in 39/49 vs 12/13; p=0.431), although follow-up duration was significantly longer in relapsing (median 84mo, range 14–137mo) than in monophasic patients (median 32mo, range 4–108mo; p=0.002). Interpretation: Relapses may occur in about one-fifth of children with anti-NMDAR encephalitis, are generally milder than at onset, and may span over a long period, although they do not seem to be associated with severity in the acute phase or with outcome at follow-up. Aggressive immune therapy at onset may reduce risk of relapse. What this paper adds: Relapses of anti-N-methyl-D-aspartate receptor encephalitis may span over a long period. Relapses were not associated with severity in the acute phase or outcome at follow-up. Aggressive immune therapy at onset appears to decrease risk of relapse.

Among the significant advances in mobile network technology, as evident in the latest 3GPP releases, one of the most notable is the possibility to do aggregation between licensed and unlicensed carriers. With LTE transmitting over unlicensed bands, obvious concerns of a fair co- existence with other preexisting technologies have risen up. In this study, we aim to evaluate the impact of LTE transmission on the key mechanism of Clear Channel Assessment (CCA), which is common to several unlicensed systems, amongst which Wi-Fi is the most notable. Relying on the statistical tool of stochastic geometry and a semianalytical approach, we will obtain the probabilities of Wi-Fi preamble false alarm and detection under a wide set of realistic propagation effects, such as path-loss and Rayleigh distributed fading. Above all, we will model the effect of a single LTE downlink interfering transmission, as well as the aggregate interference effect. Hence, we shall be able to evaluate the modified energy detection threshold that has been long debated between 3GPP and IEEE 802.11 Working Groups.

In the present work, innovative analytical techniques, such as an amperometric electronic tongue and a commercial electronic nose were used, together with spectrophotometric methods, to predict sensorial descriptors of Italian red dry wines of different denominations of origin. Genetic Algorithms were employed to select variables and build predictive regression models. On the selected models, an accurate validation technique (the Bootstrap procedure) and a procedure for the detection of outliers (Williams plot) were applied. The results obtained demonstrate the possibility of using these innovative techniques in order to describe and predict a large part of the selected sensorial information. It was not possible to build an acceptable regression model for only one descriptor, sourness. The proposed analytical methods have the advantage of being rapid and objective; furthermore, the statistical methods applied could be considered a rational operative procedure for building regression models with real predictive capability. (c) 2005 Elsevier Ltd. All rights reserved.

Classification and influence matrix analysis (CAIMAN) is a new classification method, recently proposed and based on the influence matrix (also called leverage matrix). Depending on the purposes of the classification analysis, CAIMAN can be used in three outlines: (1) D-CAIMAN is a discriminant classification method, (2) M-CAIMAN is a class modelling method allowing a sample to be classified, not classified at all, or assigned to more than one class (confused) and (3) A-CAIMAN deals with the asymmetric case, where only a reference class needs to be modelled. In this work, the geographic classification of samples of wine and olive oil has been carried out by means of CAIMAN and its results compared with discriminant analysis, by focusing great attention on the model predictive capabilities. The geographic characterization has been carried out on three different datasets: extra virgin olive oils produced in a small area, with a "protected denomination of origin" label, wines with different denominations of origin, but produced in enclosed geographical areas, and olive oils belonging to different production areas. Final results seem to indicate that the application of CAIMAN to the geographical origin identification offers several advantages: first, it shows - on an average basis - good performances; second, it is able to deal in a simple way classification problems related to tipicity, authenticity, and uniqueness characterization, which are of increasing interest in food quality issues. © 2006 Elsevier B.V. All rights reserved.