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Oxidative stress (OS) is implicated in several chronic diseases. Extra-cellular superoxide dismutase (ec-SOD) catalyses the dismutation of superoxide anions with a protective role in endothelial cells. In chronic kidney disease (CKD), OS and thyroid dysfunction (low fT3 syndrome) are frequently present, but their relationship has not yet been investigated. This cohort study evaluated ec-SOD activity in CKD patients during haemodialysis, divided into "acute haemodialytic patients" (AH, 1-3 months of treatment) and "chronic haemodialytic patients" (CH, treated for a longer period). We also evaluated plasmatic total antioxidant capacity (TAC) and its relationships with thyroid hormones. Two basal samples ("basal 1", obtained 3 days after the last dialysis; and "basal 2", obtained 2 days after the last dialysis) were collected. On the same day of basal 2, a sample was collected 5 and 10 min after the standard heparin dose and at the end of the procedure. The ec-SOD values were significantly higher in CH vs. AH in all determinations. Moreover, the same patients had lower TAC values. When the CH patients were divided into two subgroups according to fT3 levels (normal or low), we found significantly lower ec-SOD values in the group with low fT3 in the basal, 5, and 10 min samples. A significant correlation was also observed between fT3 and ec-SOD in the basal 1 samples. These data, confirming OS and low fT3 syndrome in patients with CKD, suggest that low fT3 concentrations can influence ec-SOD activity and could therefore potentially contribute to endothelial oxidative damage in these patients.

Il credito documentario viene esaminato sotto un triplice profilo: storico, giuseconomico, civilistico, con attenzione alla normativa internazionale e alle applicazioni giurisprudenziali.

The focus of this manual is on the step-by-step development of certain study skills related to the mastery of content-area reading materials. An introduction includes discussion of goals, advice for organizing learning stations, guidelines for a general pretest of skills, a teacher checklist for evaluation of study-skill competencies, and suggestions for motivating students and for conducting a study-skills workshop. The major portion of the manual is devoted to the identification of objectives and activities for the following study skills: locating information, selecting information, organizing information, using graphic aids, following directions, and developing flexibility in reading. An appendix for each skill lists additional classroom activities. A list of commercial materials, a cross-reference list of reading skills and workbook pages, and a bibliography complete the volume. (KS)

dopo una prima introduzione di carattere storico, il contributo pone in luce i profili di efficienza del credito documentario in una prospettiva EAL e analizza criticamente i differenti inquadramenti giuridici proposti in dottrina a proposito del credito documentario, soffermandosi ad esaminare le novità introdotte dalle NUU

Opera enciclopedica. Digesto, Quarta edizione. Discipline privatistiche. Sezione commerciale. Aggiornamento. Vol. 6. Il volume comprende voci di diritto tributario.

Among the statistical features of financial volatility processes, the most challenging for statistical inference purposes are non-gaussianity and non-stationarity. In this study I present results from experiments aimed to empirically modeling return generating processes in which the underlying volatility dynamics are subject to changes in scaling regimes. This means that the usually observed power law behavior of self-similar and long range dependent processes might be subject to dynamics which vary with the resolution levels. The observed information are encompassed in a model based on scale-dependent information packets that reflect heterogeneous activity in the financial market. I show how the volatility structure of a stock return index can be cast in a latent variable form and suggest how this model can account for regime switching when risk and returns are explicitly linked. To this end, I find that wavelets and multi-resolution analysis are very useful in modeling volatility and in interpreting its structure, since they deliver both sparse representations and coherent decompositions, and show the multi-scaling features inherent to the data set

We present an application of wavelet techniques to non-stationary time series with the aim of detecting the dependence structure which is typically found to characterize intraday stock index financial returns. It is particularly important to identify what components truly belong to the underlying volatility process, compared with those features appearing instead as a result of the presence of disturbance processes. The latter may yield misleading inference results when standard financial time series models are adopted. There is no universal agreement on whether long memory really affects financial series, or instead whether it could be that non-stationarity, once detected and accounted for, may allow for more power in detecting the dependence structure and thus suggest more reliable models. Wavelets are still a novel tool in the domain of applications in finance; thus, one goal is to try to show their potential use for signal decomposition and approximation of time-frequency signals. This might suggest a better interpretation of multi-scaling and aggregation effects in high-frequency returns. We show, by using special dictionaries of functions and ad hoc algorithms, that a pre-processing procedure for stock index returns leads to a more accurate identification of dependent and non-stationary features, whose detection results are improved compared with those obtained by other traditional Fourier-based methods. This allows generalized autoregressive conditional heteroscedastic models to be more effective for statistical estimation purposes.

We show that decomposing a class of signals with overcomplete dictionaries of functions and combining multiresolution and independent component analysis allow for feature detection in complex non-stationary high frequency time series. Computational learning techniques are then designed through the Matching Pursuit algorithm, whose performance is monitored so to extract relevant information about the structure of the volatility function. We refer to wavelet and cosine packet dictionaries due to the fact that with intra-daily time series some features of the underlying stochastic processes may remain undetected when standard volatility models are applied to the observed data. Independent component analysis results are particularly encouraging and suggest a better compromise between time and frequency resolutions, and thus a more efficient and accurate Matching Pursuit performance.

We show that decomposing a class of signals with overcomplete dictionaries of functions and combining multiresolution and independent component analysis allow for feature detection in complex non-stationary high frequency time series. Computational learning techniques are then designed through the Matching Pursuit algorithm, whose performance is monitored so to extract relevant information about the structure of the volatility function. We refer to wavelet and cosine packet dictionaries due to the fact that with intra-daily time series some features of the underlying stochastic processes may remain undetected when standard volatility models are applied to the observed data. Independent component analysis results are particularly encouraging and suggest a better compromise between time and frequency resolutions, and thus a more efficient and accurate Matching Pursuit performance.