Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model

OBJECTIVE: Accuracy and precision of the statistical analysis methods used for brain activation maps are essential. Adjusting models to consider spatiotemporal correlation embedded in fMRI data may increase their accuracy, but it also introduces a high computational cost. The present study aimed to apply and assess the spatiotemporal Gaussian process (STGP) model to improve accuracy and reduce cost. METHODS: We applied the spatiotemporal Gaussian process (STGP) model for both simulated and experimental memory tfMRI data and compared the findings with fast, fully Bayesian, and General Linear Models (GLM). To assess their accuracy and precision, the models were fitted to the simulated data (1000 voxels,100 times point for 50 people), and an average of accuracy indexes of 100 repetitions was computed. Functional and activation maps for all models were calculated in experimental data analysis. RESULTS: STGP model resulted in a higher Z-score in the whole brain, in the 1000 most activated voxels, and in the frontal lobe as the approved memory area. Based on the simulated data, the STGP model showed more accuracy and precision than the other two models. However, its computational time was more than the GLM, as the price of model correction, but much less than that of the fast, fully Bayesian model. CONCLUSION: Spatiotemporal correlation further improved the accuracy of the STGP compared to the GLM and fast, fully Bayesian model. This can result in more accurate activation maps. Moreover, the STGP model’s computational speed appears to be reasonable for model application

Tracking analysis of minimum kernel risk-sensitive loss algorithm under general non-Gaussian noise

In this paper the steady-state tracking performance of minimum kernel risk-sensitive loss (MKRSL) in a non-stationary environment is analyzed. In order to model a non-stationary environment, a first-order random-walk model is used to describe the variations of optimum weight vector over time. Moreover, the measurement noise is considered to have non-Gaussian distribution. The energy conservation relation is utilized to extract an approximate closed-form expression for the steady-state excess mean square error (EMSE). Our analysis shows that unlike for the stationary case, the EMSE curve is not an increasing function of step-size parameter. Hence, the optimum step-size which minimizes the EMSE is derived. We also discuss that our approach can be used to extract steady-state EMSE for a general class of adaptive filters. The simulation results with different noise distributions support the theoretical derivations

SONDEO ARQUEOLÓGICO SECTOR 70 [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Landforms identification using neural network-self organizing map and SRTM data

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic features is a challenging task and could provide useful information for landscape studies at different scales. In this study the 3 arc second SRTM digital elevation model was projected on a UTM grid with 90 meter spacing for a mountainous terrain at the Polish - Ukrainian border. Terrain parameters (morphometric parameters) such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived by fitting a bivariate quadratic surface with a window size of 5×5 corresponding to 450 meters on the ground. These morphometric parameters are strongly related to topographic features and geomorphological processes. Such data allow us to enumerate topographic features in a way meaningful for landscape analysis. Kohonen Self Organizing Map (SOM) as an unsupervised neural network algorithm is used for classification of these morphometric parameters into 10 classes representing landforms elements such as ridge, channel, crest line, planar and valley bottom. These classes were analyzed and interpreted based on spectral signature, feature space, and 3D presentations of the area. Texture contents were enhanced by separating the 10 classes into individual maps and applying occurrence filters with 9×9 window to each map. This procedure resulted in 10 new inputs to the SOM. Again SOM was trained and a map with four dominant landforms, mountains with steep slopes, plane areas with gentle slopes, dissected ridges and lower valleys with moderate to very steep slopes and main valleys with gentle to moderate slopes was produced. Both landform maps were evaluated by superimposing contour lines. Results showed that Self Organizing Map is a very promising and efficient tool for land form identification. There is a very good agreement between identified landforms and contour lines. This new procedure is encouraging and offers new possibilities in the study of both type of terrain features, general landforms and landform elements

Guillian-Barre syndrome , Childhood, Epidemiology, Electrodiagnosis, Clinical features, East Azarbaijan

 ObjectiveThis study aims at determining the epidemiologic, presenting symptoms, clinical course and electrophysiologic features of childhood Guillain-Barre Syndrome (GBS) in the East Azarbaijan province over a period of five years.Materials & Methods All the patients, aged< 15 years, referred/admitted to Tabriz Children Hospital with GBS between January 2001 and December 2005 were investigated.ResultsOne hundred and twelve subjects were enrolled during this period. The average annual incidence rate was 2.21 per 100000 population of children agedConclusion The axonal type of GBS is a relatively common form of childhood GBS occurring in East Azerbaijan.

Determinants of burnout and stress on students health: a study of Iranian expatriate international students

Current research explore analysing the prevalence of stress and burnout among Iranian international students and the partnership of stress and burnout with their health status. The results shows that stress and burnout among international students is a valid event. The conclusions shows that burnout and stress among international students is a valid event. Upon investigation, study workload to be the prime stressor. Because of the close relationship burnout and stress have with health position of the individuals, Because of the close relationship stress and burnout have with health position of the individuals, ways of reducing the international students' workload and help with emotional exhaustion recommended before it causes a detrimental amount of burnout. Results didn't determine any relationship among demographic characteristics of individuals and their stress/burnout event

Evaluation of the Efficiency of CROPWAT Model for Determining Plant Water Requirement in Arid Regions

Shortage of water resources and increasing demand to consumption of this scarce resource, leads to somenoticeable limitations. On the other hand, population growth and consequently, increasing demand for water in aridand semi arid regions , needs production in exchange of little amount of water consumption. To approach thisobjective, an experiment in the complete randomized blocks carried out in four replications for cumin plant growingin Zabol, southeastern Iran. Experimental treatments included irrigation periods at three levels. Then usingCROPWAT model, the water requirement of the plant is met. Analyzing the data resulted from production gatheredin different times of irrigation and consumption of water in the three times irrigation case with sound efficiency (1750m3/ha), is more little than the water amount which is simulated by the CROPWAT model in 2003 (6070 m3/ha) and(5363 m3/ha) in 2004. It then showed that this model is not effective in determining the water requirement of cumin atthis region

A genome-wide scan of wastewater E. coli for genes under positive selection: focusing on mechanisms of antibiotic resistance

Antibiotic resistance is a global health threat and consequently, there is a need to understand the mechanisms driving its emergence. Here, we hypothesize that genes and mutations under positive selection may contribute to antibiotic resistance. We explored wastewater E. coli, whose genomes are highly diverse. We subjected 92 genomes to a statistical analysis for positively selected genes. We obtained 75 genes under positive selection and explored their potential for antibiotic resistance. We found that eight genes have functions relating to antibiotic resistance, such as biofilm formation, membrane permeability, and bacterial persistence. Finally, we correlated the presence/absence of non-synonymous mutations in positively selected sites of the genes with a function in resistance against 20 most prescribed antibiotics. We identified mutations associated with antibiotic resistance in two genes: the porin ompC and the bacterial persistence gene hipA. These mutations are located at the surface of the proteins and may hence have a direct effect on structure and function. For hipA, we hypothesize that the mutations influence its interaction with hipB and that they enhance the capacity for dormancy as a strategy to evade antibiotics. Overall, genomic data and positive selection analyses uncover novel insights into mechanisms driving antibiotic resistance