Anti-influenza antibody level after vaccination in north of Iran

Objective:Influenza is a highly transmitted disease and about 10% of the world's population is affected by this disease annually. The aim of this research was to study the variation of serum antibody levels among subjects who had already been vaccinated against influenza. Methods And Materials:This descriptive-analytical study was carried out on 196 subjects who had influenza vaccination (influvac 2005/2006) and on 200 subjects who were matched with the vaccinated subjects by their ages in Gorgan which is located in the northeast of Iran. The subject's sera were prepared seven weeks after the influenza vaccination. Their serum antibody levels were determinated by the heamaglutination inhibition test. Results:The antibody titre in 81 subjects of the vaccinated group and in 175 subjects of the control group was less then 1/40. The mean antibody titre of the vaccinated subjects and the control group was 143.4±10.89 and 18.34±3.2, respectively. The difference was statistically significant (P value=0.000). Conclusion:The findings showed that the mean titre of the antibodies in the vaccinated and control groups was statistically different. This means that the influenza vaccine has good efficacy in our population

Robust phase retrieval with the swept approximate message passing (prSAMP) algorithm

In phase retrieval, the goal is to recover a complex signal from the magnitude of its linear measurements. While many well-known algorithms guarantee deterministic recovery of the unknown signal using i.i.d. random measurement matrices, they suffer serious convergence issues some ill-conditioned matrices. As an example, this happens in optical imagers using binary intensity-only spatial light modulators to shape the input wavefront. The problem of ill-conditioned measurement matrices has also been a topic of interest for compressed sensing researchers during the past decade. In this paper, using recent advances in generic compressed sensing, we propose a new phase retrieval algorithm that well-adopts for both Gaussian i.i.d. and binary matrices using both sparse and dense input signals. This algorithm is also robust to the strong noise levels found in some imaging applications

Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas