Optimal pseudorandom sequence selection for online c-VEP based BCI control applications

<div><p>Background</p><p>In a c-VEP BCI setting, test subjects can have highly varying performances when different pseudorandom sequences are applied as stimulus, and ideally, multiple codes should be supported. On the other hand, repeating the experiment with many different pseudorandom sequences is a laborious process.</p><p>Aims</p><p>This study aimed to suggest an efficient method for choosing the optimal stimulus sequence based on a fast test and simple measures to increase the performance and minimize the time consumption for research trials.</p><p>Methods</p><p>A total of 21 healthy subjects were included in an online wheelchair control task and completed the same task using stimuli based on the m-code, the gold-code, and the Barker-code. Correct/incorrect identification and time consumption were obtained for each identification. Subject-specific templates were characterized and used in a forward-step first-order model to predict the chance of completion and accuracy score.</p><p>Results</p><p>No specific pseudorandom sequence showed superior accuracy on the group basis. When isolating the individual performances with the highest accuracy, time consumption per identification was not significantly increased. The Accuracy Score aids in predicting what pseudorandom sequence will lead to the best performance using only the templates. The Accuracy Score was higher when the template resembled a delta function the most and when repeated templates were consistent. For completion prediction, only the shape of the template was a significant predictor.</p><p>Conclusions</p><p>The simple and fast method presented in this study as the Accuracy Score, allows c-VEP based BCI systems to support multiple pseudorandom sequences without increase in trial length. This allows for more personalized BCI systems with better performance to be tested without increased costs.</p></div

The fluctuations of physicochemical factors and phytoplankton populations of Urmia Lake, Iran

Urmia Lake is one of the two large hypersaline lakes in the world which have Artemia. It is located in northwest of Iran. Due to a decrease in water inflow and volume, the salinity of Urmia Lake has reached to more than 300 g.l-1 since 2001. The increased salinity has greatly influenced biological aspects of the lake, and caused the lake undergoes at critical conduction. The aim of the present study was to investigate the distribution fluctuations of phytoplanktons and selected physicochemical factors in relation to Artemia distribution in Urmia Lake during 8 months. Statistical analysis of mean values of ion concentrations and phytoplankton abundance indicated significant differences among sampling months. The minimum and maximum values for the selected factors were, as Cl- 176.2-201.3 g.l-1 , CO2 95-175mg.l-1 , dissolved oxygen (DO) 0.1-2.8 mg.l-1 , HCO3 - 144-496 mg.l-1 , PO42+ 104-875 mg.l-1, NO3- 330-4104 mg.l-1, NO2- 4-21.5 mg.l-1, SO42- 10490-29840 mg.l-1, Ca2+ 561-1606 mg.l-1, Mg2+ 3649-14587 mg.l-1 while water hardness was 21000- 62000 mg.l-1. Fourteen phytoplankton genera included Bacillariophyceae (10 genera),Chlorophyceae (2 genera) and Cyanophyceae (2 genera) were identified during sampling period. The smallest average density of phytoplankton 97249 L-1 was observed in December 2005 and the greatest average density 481983 L-1 in August 2005. Dunaliella sp. composed 92.1% of the lake's phytoplankton. Statistical analysis of phytoplanktons fluctuations showed a significant difference among different months (p< 0.05)

Impaired expression of key molecules of ammoniagenesis underlies renal acidosis in a rat model of chronic kidney disease

Background Advanced chronic kidney disease (CKD) is associated with the development of renal metabolic acidosis. Metabolic acidosis per se may represent a trigger for progression of CKD. Renal acidosis of CKD is characterized by low urinary ammonium excretion with preserved urinary acidification indicating a defect in renal ammoniagenesis, ammonia excretion or both. The underlying molecular mechanisms, however, have not been addressed to date. Methods We examined the Han:SPRD rat model and used a combination of metabolic studies, mRNA and protein analysis of renal molecules involved in acid-base handling. Results We demonstrate that rats with reduced kidney function as evident from lower creatinine clearance, lower haematocrit, higher plasma blood urea nitrogen, creatinine, phosphate and potassium had metabolic acidosis that could be aggravated by HCl acid loading. Urinary ammonium excretion was highly reduced whereas urinary pH was more acidic in CKD compared with control animals. The abundance of key enzymes and transporters of proximal tubular ammoniagenesis (phosphate-dependent glutaminase, PEPCK and SNAT3) and bicarbonate transport (NBCe1) was reduced in CKD compared with control animals. In the collecting duct, normal expression of the B1 H+-ATPase subunit is in agreement with low urinary pH. In contrast, the RhCG ammonia transporter, critical for the final secretion of ammonia into urine was strongly down-regulated in CKD animals. Conclusion In the Han:SPRD rat model for CKD, key molecules required for renal ammoniagenesis and ammonia excretion are highly down-regulated providing a possible molecular explanation for the development and maintenance of renal acidosis in CKD patient

Investigation and determination of marine biotoxins in the shellfish of Persian Gulf and Oman Sea

Marine algal toxins have drawn worldwide attention because of their involvement in human intoxication and the socio-economic impacts. Marine biotoxins have been produced by harmful bloom algae, known as dinoflagellate. In the present study, two groups of toxins, i.e. PSP, ASP analyzed in the muscle of shellfish caught from the north parts of the Persian Gulf (Bandar Abbas, Bandar Lengeh, Boushehr) and Oman Sea (Chabahar). Sample preparation and extraction were done according to AOAC methods and by ELISA. PSP amounts in the shellfish samples ranged from ND-3.962 and ND-1.477 mg/g muscle. The results showed all samples were safe

A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations

Abstract Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. Results A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. Conclusions The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.</p

PCBs and DDTs in surface mangrove sediments from the south of Iran (ID NO. 048)

Mangrove sediments were collected during wet and dry seasons from nine stations in Khamir, Laft and natural reservoir mangrove-dense areas of Hormozgan province in the south of Iran. Σ PCBs ranged from 5.33 to 15.5 ng/g dry weight and the dominant congener was no.153. Average Σ DDTs for Khamir and Laft mangroves were 16.58 ± 1.51 and 18.8 ± 9.98 ng/g dry weight. DDT was more abundant than DDE and DDD isomers, which indicated the input of DDT. The sediment quality guideline shows that the concentration of Σ PCBs were below the ER-M guideline, whereas levels of Σ DDTs were between ER-L and ER-M. The mangrove ecosystem in Hormozgan province is suffering from urban and industrial development

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months

Colocalization of the (Pro)renin receptor/Atp6ap2 with H+-AT pases in mouse kidney but prorenin does not acutely regulate intercalated cell H+-ATPase activity

The (Pro)renin receptor (P)RR/Atp6ap2 is a cell surface protein capable of binding and nonproteolytically activate prorenin. Additionally, (P)RR is associated with H+-ATPases and alternative functions in H+-ATPase regulation as well as inWnt signalling have been reported. Kidneys express very high levels of H+-ATPases which are involved in multiple functions such as endocytosis, membrane protein recycling as well as urinary acidification, bicarbonate reabsorption, and salt absorption. Here, we wanted to localize the (P)RR/Atp6ap2 along the murine nephron, exmaine whether the (P)RR/Atp6ap2 is co