Heavy Metals in Mussels (Mytilus galloprovincialis) from the Ionian Sea, Italy

Concentrations of six heavy metals (Hg, Pb, Cd, Cr, Zn, and Sn) were determined in mussels (Mytilus galloprovincialis) collected between June and September 1997 from 10 locations along a sound formed by two inlets (Mar Piccolo) near the Gulf of Taranto (Ionian Sea, Italy). The average concentrations of the heavy metals found in mussels samples were 0.15 mg/kg for Hg, 1.19 mg/kg for Pb, 0.64 mg/kg for Cd, 0.31 mg/kg for Cr, 5.15 mg/kg for Zn, and 0.54 mg/kg for Sn. The concentrations of heavy metals in mussels from the first inlet did not differ greatly from those observed in mussels from the second inlet. The concentrations of heavy metals in the mussels analyzed were below acceptable levels for human consumption

Occurrence of heavy metals (Hg, Cd, and Pb) and polychlorinated biphenyls in salted anchovies.

The most popular brands of salted anchovies (Engraulis encrasicolus) from the Mediterranean Sea and Atlantic Ocean were purchased from several Italian supermarkets and grocery stores. Heavy metal (Hg, Cd, and Pb) and polychlorinated biphenyl (PCB) levels were determined and assessed by comparing the concentrations in these samples with the maximum permissible limits set by the European Union (Reg EC 629/2008 and Reg EC 1881/2006 [Off. J. Eur. Union L 173:3-9 and 364:5-24, respectively]). The Hg and Cd levels were higher than those of Pb in all samples examined. For Hg and Pb, the concentrations recorded in this study were below the authorized limits, while an appreciable percentage of samples from both locations (Mediterranean Sea, 35%, and Atlantic Ocean, 25%) showed Cd levels exceeding the threshold recommended for human consumption. Concerning PCBs, the results of principal component analysis showed that samples from the two different marine areas appeared to be discriminate, with Mediterranean anchovies more contaminated than the others, in spite of their lower lipid content. However, anchovy samples from both locations had dioxinlike-PCB levels (Mediterranean Sea, 0.011 pg World Health Organization toxic equivalency [WHO-TEQ] g(-1), wet weight, and Atlantic Ocean, 0.007 pg WHO-TEQ g(-1), wet weight) that were below the WHO-TEQ maximum concentration set by European regulation. The results of this study will help in generating data needed for the assessment of heavy metal and PCB intake from this food

Trace Metals in Pork Meat Products Marketed in Italy: Occurrence and Health Risk Characterization

This study provides valuable information on the levels of various trace metals (Pb, Cd, Hg, Zn, Cu, Cr) in meat products (baked ham, raw ham, mortadella, cured sausage, würstel, salami) from South Italy and calculates potential health risk toxicity associated with their consumption for the total population and for children. In the samples studied metal concentrations are within the permissible legal limits (Cd: 0.01–0.03 μg g−1 w.w., Hg: 0.01–0.02 μg g−1 w.w., Zn: 5.71–7.32 μg g−1 w.w., Cu: 1.08–1.21 μg g−1 w.w., Cr: 0.15–0.23 μg g−1 w.w.), except for Pb (Pb: 0.22–0.38 μg g−1 w.w.). The estimated intake values are within the provisional tolerable daily intake limits for toxic metals and recommended daily intake values for essential metals in both tested groups. The noncarcinogenic risk values of the individual metals indicate that there is no health risk, but their combined effects might constitute a potential risk for children. Furthermore, the cumulative cancer risk of all samples studied exceeds the recommended threshold risk limit (> 10−4) in both total population and children, indicating a risk of potential health problems for consumers especially for children, who are more vulnerable to toxic metal exposure

Geodesic acoustic modes in a fluid model of tokamak plasma : the effects of finite beta and collisionality

Starting from the Braginskii equations, relevant for the tokamak edge region, a complete set of nonlinear equations for the geodesic acoustic modes (GAM) has been derived which includes collisionality, plasma beta and external sources of particle, momentum and heat. Local linear analysis shows that the GAM frequency increases with collisionality at low radial wave number $k_{r}$ and decreases at high $k_{r}$. GAM frequency also decreases with plasma beta. Radial profiles of GAM frequency for two Tore Supra shots, which were part of a collisionality scan, are compared with these calculations. Discrepency between experiment and theory is observed, which seems to be explained by a finite $k_{r}$ for the GAM when flux surface averaged density $\langle n \rangle$ and temperature $\langle T \rangle$ are assumed to vanish. It is shown that this agreement is incidental and self-consistent inclusion of $\langle n \rangle$ and $\langle T \rangle$ responses enhances the disagreement more with $k_r$ at high $k_{r}$ . So the discrepancy between the linear GAM calculation, (which persist also for more "complete" linear models such as gyrokinetics) can probably not be resolved by simply adding a finite $k_{r}$

Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation

In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks are introduced to upsize combined feature maps to leverage information from lesion shape and location. We trained and tested the proposed model using orthogonal plane orientations of each 3D modality to exploit the contextual information in all directions. The proposed pipeline is evaluated on two different datasets: a private dataset including 37 MS patients and a publicly available dataset known as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset, consisting of 14 MS patients. Considering the ISBI challenge, at the time of submission, our method was amongst the top performing solutions. On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag

Étude du transport turbulent dans les plasmas du tokamak Tore Supra : observation des écoulements perpendiculaires stationnaires et du modeacoustique géodésique

Tokamaks are toroidal machines designed to magnetically confine plasma and to host reactions of nuclear fusion. Their performance is limited by turbulent transport, which precise mechanisms are not fully elucidated. Turbulent fluctuations cause the onset of macroscopic mean flows, in the direction perpendicular to both magnetic field and small radius, called zonal flows. Their shear is known to decrease both fluctuation level and transport. Diagnostics of Doppler backscattering have been installed on Tore Supra (CEA Cadarache). They could measure the perpendicular velocity of turbulent fluctuations in different zones of the plasma.First, it is shown that, on average, fluctuations flow twice as fast at the low field side of the machine than at its top. The difference, significantly higher than predicted by common models, is put in perspective with the properties of magnetic equilibrium and the behaviour of fluctuations.Then, variations of the velocity, due to the zonal flow branch called geodesic acoustic mode (GAM), are examined in a series of experiments where collisionality is varied. Those observations have been quantitatively compared with a gyrokinetic simulation with the code GYSELA, using the equilibrium profiles from the experiments. GAM frequency in experiments is lower than predicted, especially at low collisionalities. In addition, the instationarity of GAM has been characterized with the Hilbert-Huang transform. Burts of GAM oscillations have similar statistics between experiments and simulation and their duration could be related with the nonlinear interaction between the GAM and turbulence and with the continuum damping which is observed in the simulation.Les tokamaks, machines toroïdales à confinement magnétique conçues pour porter un plasma aux conditions nécessaires à l'obtention de réactions de fusion, ont des performances limitées par le transport turbulent, dont les mécanismes ne sont pas tous élucidés. Les fluctuations turbulentes alimentent des écoulements macroscopiques, dits flots zonaux, dont le cisaillement réduit en retour le niveau de fluctuation. Des dispositifs de rétrodiffusion Doppler ont été installés sur Tore Supra (CEA Cadarache) et permettent de mesurer la vitesse perpendiculaire des fluctuations.Premièrement, il est montré que la vitesse moyenne est près de deux fois supérieure du côté externe du plasma que dans sa partie supérieure, au-delà des prédictions conventionnelles. Ces asymétries sont mises en perspective avec certains aspects de l'équilibre magnétique et des caractéristiques des fluctuations.Puis, les oscillations de la vitesse, attribuées à la branche des flots zonaux dite mode géodésique acoustique (GAM), ont été caractérisées dans des expériences où la collisionalité varie. Elles sont comparées aux prédictions théoriques et aux résultats d'une simulation avec le code gyrocinétique GYSELA, utilisant les profils expérimentaux d'équilibre. La fréquence du GAM dans l'expérience est plus faible que prédit, en particulier aux plus basses collisionalités. Les variations de son intensité, détectées avec la transformée de Hilbert-Huang, montrent des bouffées d'oscillations de distributions analogues entre expérience et simulation, et dont la durée pourrait être liée à son interaction non-linéaire avec la turbulence ainsi qu'au phénomène d'amortissement de continuum observé dans la simulation

In vitro and in vivo evaluation of a new active heat moisture exchanger.

INTRODUCTION: In order to improve the efficiency of heat moisture exchangers (HMEs), new hybrid humidifiers (active HMEs) that add water and heat to HMEs have been developed. In this study we evaluated the efficiency, both in vitro and in vivo, of a new active HME (the Performer; StarMed, Mirandola, Italy) as compared with that of existing HMEs (Hygroster and Hygrobac; Mallinckrodt, Mirandola, Italy). METHODS: We tested the efficiency by measuring the temperature and absolute humidity (AH) in vitro using a test lung ventilated at three levels of minute ventilation (5, 10 and 15 l/min) and at two tidal volumes (0.5 and 1 l), and in vivo in 42 patients with acute lung injury (arterial oxygen tension/fractional inspired oxygen ratio 283 +/- 72 mmHg). We also evaluated the efficiency in vivo after 12 hours. RESULTS: In vitro, passive Performer and Hygrobac had higher airway temperature and AH (29.2 +/- 0.7 degrees C and 29.2 +/- 0.5 degrees C, [P < 0.05]; AH: 28.9 +/- 1.6 mgH2O/l and 28.1 +/- 0.8 mgH2O/l, [P < 0.05]) than did Hygroster (airway temperature: 28.1 +/- 0.3 degrees C [P < 0.05]; AH: 27 +/- 1.2 mgH2O/l [P < 0.05]). Both devices suffered a loss of efficiency at the highest minute ventilation and tidal volume, and at the lowest minute ventilation. Active Performer had higher airway temperature and AH (31.9 +/- 0.3 degrees C and 34.3 +/- 0.6 mgH2O/l; [P < 0.05]) than did Hygrobac and Hygroster, and was not influenced by minute ventilation or tidal volume. In vivo, the efficiency of passive Performer was similar to that of Hygrobac but better than Hygroster, whereas Active Performer was better than both. The active Performer exhibited good efficiency when used for up to 12 hours in vivo. CONCLUSION: This study showed that active Performer may provide adequate conditioning of inspired gases, both as a passive and as an active device

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS