Model-based analysis and forecast of sleep-wake regulatory dynamics: Tools and applications to data.

Extensive clinical and experimental evidence links sleep-wake regulation and state of vigilance (SOV) to neurological disorders including schizophrenia and epilepsy. To understand the bidirectional coupling between disease severity and sleep disturbances, we need to investigate the underlying neurophysiological interactions of the sleep-wake regulatory system (SWRS) in normal and pathological brains. We utilized unscented Kalman filter based data assimilation (DA) and physiologically based mathematical models of a sleep-wake regulatory network synchronized with experimental measurements to reconstruct and predict the state of SWRS in chronically implanted animals. Critical to applying this technique to real biological systems is the need to estimate the underlying model parameters. We have developed an estimation method capable of simultaneously fitting and tracking multiple model parameters to optimize the reconstructed system state. We add to this fixed-lag smoothing to improve reconstruction of random input to the system and those that have a delayed effect on the observed dynamics. To demonstrate application of our DA framework, we have experimentally recorded brain activity from freely behaving rodents and classified discrete SOV continuously for many-day long recordings. These discretized observations were then used as the "noisy observables" in the implemented framework to estimate time-dependent model parameters and then to forecast future state and state transitions from out-of-sample recordings.

miR-17-92 host gene, uderexpressed in gastric cancer and its expression was negatively correlated with the metastasis.

BACKGROUND: This study aimed to evaluate the expression of miR-17-92 host gene (MIR17HG), in gastric cancer and paired normal adjacent tissues for the 1st time. METHODS: Using quantitative real-time-polymerase chain reaction, the MIR17HG expression was assessed in 30 paired tumoral and nontumoral gastric tissue samples. RESULTS: Our results showed that this transcript was significantly underexpressed in gastric tumors compared with normal ones. Furthermore, there was an association between the expression levels of MIR17HG and gastric cancer grades and stages. Moreover, the expression level of MIR17HG was conversely associated with the size of tumoral specimens in early stages (stages I and II). We also observed an association between the presence of metastasis and lower expression of MIR17HG. CONCLUSION: Our results suggest that MIR17HG gene expression is dysregulated in gastric cancer in which it may indicate a tumor suppressive function of this miRNA cluster host gene in gastric cancer.

A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon.

In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R(2)) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively.

The impact of mixed, hope and forgiveness-focused marital counselling on interpersonal cognitive distortions of couples filing for divorce.

Divorce and conflict are overlapping processes. Previous findings suggest that spirituality-related interventions in mental health nursing may play a significant role in reducing the level and amount of conflict. We examined the effects of hope and forgiveness-focused marital counselling and a combination of the two intervention types on interpersonal cognitive distortions of couples filing for divorce in Isfahan, Iran. We conducted a quasi-experimental study with a pre-test and post-test design. Of 440 couples referred to the Crisis Intervention Center undergoing pre-divorce counselling, 60 were randomly assigned to four groups: hope-focused, forgiveness-focused, mixed and control. Data were gathered using the Interpersonal Cognitive Distortions Scale and analysed using the non-parametric Kruskal-Wallis, Mann-Whitney's U and Wilcoxon tests. Hope- and forgiveness-focused interventions did not have a significant effect on the total number of interpersonal cognitive distortions in comparison with the control group. However, the mixed intervention significantly reduced irrational expectations and interpersonal rejection among couples. Combining hope- and forgiveness-focused interventions can be used to decrease irrational marital beliefs among couples. In addition, rating the level of conflict among couples is important for determining the type of intervention that should be used by mental health nurses (psycho-educational or therapeutic).