Algoritmi per l'analisi dinamica della connettività neurale in vitro: applicazione a disturbi dello sviluppo

Studio della formazione e dell'evoluzione di reti neurali in vitro, usate come modello delle reti in vivo, e loro caratterizzazione morfologica e topologic

Data Mining of Biomedical Databases

Data mining can be defined as the nontrivial extraction of implicit, previously unknown and potentially useful information from data. This thesis is focused on Data Mining in Biomedicine, representing one of the most interesting fields of application. Different kinds of biomedical data sets would require different data mining approaches. Two approaches are treated in this thesis, divided in two separate and independent parts. The first part deals with Bayesian Networks, representing one of the most successful tools for medical diagnosis and therapies follow-up. Formally, a Bayesian Network (BN) is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. An algorithm for Bayesian network structure learning that is a variation of the standard search-and-score approach has been developed. The proposed approach overcomes the creation of redundant network structures that may include non significant connections between variables. In particular, the algorithm finds which relationships between the variables must be prevented, by exploiting the binarization of a square matrix containing the mutual information (MI) among all pairs of variables. Four different binarization methods are implemented. The MI binary matrix is exploited as a pre-conditioning step for the subsequent greedy search procedure that optimizes the network score, reducing the number of possible search paths in the greedy search procedure. This approach has been tested on four different datasets and compared against the standard search-and-score algorithm as implemented in the DEAL package, with successful results. Moreover, a comparison among different network scores has been performed. The second part of this thesis is focused on data mining of microarray databases. An algorithm able to perform the analysis of Illumina microRNA microarray data in a systematic and easy way has been developed. The algorithm includes two parts. The first part is the pre-processing, characterized by two steps: variance stabilization and normalization. Variance stabilization has to be performed to abrogate or at least reduce the heteroskedasticity while normalization has to be performed to minimize systematic effects that are not constant among different samples of an experiment and that are not due to the factors under investigation. Three alternative variance stabilization strategies and three alternative normalization approaches are included. So, considering all the possible combinations between variance stabilization and normalization strategies, 9 different ways to pre-process the data are obtained. The second part of the algorithm deals with the statistical analysis for the differential expression detection. Linear models and empirical Bayes methods are used. The final result is the list of the microRNAs significantly differentially-expressed in two different conditions. The algorithm has been tested on three different real datasets and partially validated with an independent approach (quantitative real time PCR). Moreover, the influence of the use of different preprocessing methods on the discovery of differentially expressed microRNAs has been studied and a comparison among the different normalization methods has been performed. This is the first study comparing normalization techniques for Illumina microRNA microarray data

Effect of Inversion Recovery Fat Suppression on Hepatic R2* Quantitation in Transfusional Siderosis

OBJECTIVE. The purpose of this study is to evaluate whether the application of spectral pre-saturation inversion recovery (SPIR) fat suppression in standard multiecho gradient-echo sequences has a significant effect on hepatic R2* quantitation in patients with iron overload syndromes. MATERIALS AND METHODS. Eighty patients were scanned with a multiecho gradient-echo sequence without and with the application of SPIR. Six different postprocessing approaches were used to extract R2* values for maximum generality. RESULTS. SPIR fat suppression lowered R2* values by 3.9–7.0% (p < 0.0001 in all pairwise comparisons), independently of the postprocessing algorithm. Coefficients of variation for R2* ranged from 4.5% to 10.0%. Regardless of the size of the ROI (area of homogeneous tissue or entire liver profile in the slice), pixelwise approaches combined with an exponential-plus-constant fitting model yielded the lowest coefficients of variation (4.5% and 5.1%), whereas truncated exponential fits of the averaged signals produced the highest coefficients of variation (7.8% and 10%). For R2* values exceeding 200 Hz, a Bland-Altman analysis showed a bias that grew linearly for all postprocessing methods. CONCLUSION. SPIR fat suppression resulted in systematically lower hepatic R2* estimates. Because calibration curves were derived using images without fat suppression, these biases should be corrected when reporting liver iron concentrations estimated from fat-suppressed multiecho T2*-weighted images

On the Non-Inflationary Effects of Long-Term Unemployment Reductions

Contrary to the New Keynesian paradigm, long-term unemployment can be reversed without a significant uptick in inflation The paper critically examines the New Keynesian explanation of hysteresis based on the role of long-term unemployment. We first examine its analytical foundations, according to which rehiring long-term unemployed individuals would not be possible without accelerating inflation. Then we empirically assess its validity along two lines of inquiry. First, we investigate the reversibility of long-term unemployment. Then we focus on episodes of sustained long-term unemployment reductions to check for inflationary effects. Specifically, in a panel of 25 OECD countries (from 1983 to 2016), we verify by means of local projections whether they are associated with inflationary pressures in a subsequent five-year window. Two main results emerge: i) the evolution of the long-term unemployment rate is almost completely synchronous with the dynamics of the total unemployment rate, both during downswings and upswings; ii) we do not find indications of accelerating or persistently higher inflation during and after episodes of strong declines in the long- term unemployment rate, even when they occur in country-years in which the actual unemployment rate was estimated to be below a conventionally estimated Non-Accelerating Inflation Rate of Unemployment (NAIRU). Our results call into question the role of long-term unemployment in causing hysteresis and provide support to policy implications that are at variance with the conventional wisdom that regards the NAIRU as an inflationary barrier

Evaluation of posttreatment response of hepatocellular carcinoma: comparison of ultrasonography with second-generation ultrasound contrast agent and multidetector CT

We evaluated the ability of one-month follow-up contrast- enhanced ultrasound (CEUS) with second-generation contrast agent in monitoring radio frequency ablation (RFA) and transcatheter arterial chemoembolization (TACE) treatments of hepatocellular carcinoma (HCC). One-hundred forty-eight HCCs were studied using CEUS: 110 nodules were treated with RFA [41/110 RFA were performed using a pretreatment and an immediate postablation evaluation using CEUS (group 1); 69/110 using only US guidance (group 2)] and 38 nodules treated with TACE. For statistical analysis, McNemar test was used. Overall complete response was observed in 107/148 nodules (92/110 treated with RFA and 15/38 with TACE). A better rate of complete response was found in group 1 compared to group 2 (92.7% vs. 78.3%). In RFA treatment, CEUS showed a sensitivity of 83.3% and a specificity of 100% (diagnostic accuracy of 97%) using MDCT as reference standard with no statistical difference (p > 0.05). CEUS detected all cases of incomplete response in HCC treated with TACE using angiography as reference standard (diagnostic accuracy 100%).We recommend assessing residual intratumoral flow on CEUS during RFA procedure to determine the necessity of immediate additional treatment. In case of positive CEUS results, HCC treated with TACE should be considered still viable

All-optical Digital Processing Through Semiconductor Optical Amplifiers

Non

All-Optical Flip-Flops Based on Semiconductor Technologies

Non

Photonic Combinatorial Network for Contention Management in 160 Gb/s Interconnection Networks based on All-Optical 2x2 Switching Elements

A modular photonic interconnection network based on a combination of basic 2×2 all-optical nodes including a photonic combinatorial network for the packet contention management is presented. The proposed architecture is synchronous, can handle optical time division multiplexed (OTDM) packets up to 160 Gb/s, exhibits self-routing capability, and very low switching latency. In such a scenario, OTDM has to be preferred to wavelength division multiplexing (WDM) because in the former case, the instantaneous packet power carries the information related to only one bit, making the signal processing based on instantaneous nonlinear interactions between packets and control signals more efficient. Moreover, OTDM can be used in interconnection networks without caring about the propagation impairments because of the very short length (< 100 m) of the links in these networks. For such short-range networks, the packet synchronization can be solved at the network boundary in the electronic domain without the need of complex optical synchronizers. In this paper, we focus on a photonic combinatorial network able to detect the contentions, and to optically drive the contention resolution block and the switching control block. The implementation of the photonic combinatorial network is based on semiconductor devices, which makes the solution very promising in terms of compactness, stability, and power consumption. This implementation represents the first example of complex photonic combinatorial network for ultrafast digital processing. The network performance has been investigated for bit streams at 10 Gb/s in terms of bit error rate (BER) and contrast ratio. Moreover, the suitability of the 2×2 photonic node architecture exploiting the earlier mentioned combinatorial network has been verified at a bit rate up to 160 Gb/s. In this way, the potential of photonic digital processing for the next generation broad band and flexible interconnection networks has been demonstrated