Moderate Intensity Exercise Is Associated with Decreased Angiotensin-Converting Enzyme, Increased β2-Adrenergic Receptor Gene Expression and Lower Blood Pressure in Middle-Aged Men

Introduction: Exercise activity increases blood flow rate but gene expression of factors that may be involved in blood pressure changes have not yet been completely studied. The purpose of the present study was to characterize the role of aerobic exercise with the intensity of 50-65 %maximal exercise heart rate in gene expression of angiotensin converting Enzyme (ACE) and β2 -adrenergic receptor (ADRB2) in adult untrained men. Methods: Twenty untrained middle aged men (49.91±3.17 years) volunteered to participate in this study. The participants were randomly assigned to exercise (Ex, n=10) and control (Con, n=10) groups. The Ex group performed aerobic exercises for 40-50 min/day, 4 days/week for 8 weeks. Expression of ACE mRNA and ADRB2 mRNA were determined by real time PCR at the start of the exercise program, 24 hours after the last training session at the end of week 4 and at the end of week 8. Blood samples were collected following a 12-hour overnight fast and were taken between 08.00-09.00h. Descriptive statistics were determined for all variables. The normal distribution of data was determined by Kolmogorov-Smirnov test. Data were analyzed using appropriate Mixed Model Method (One-way ANOVA). Post hoc comparisons between selected means were performed with Bonferroni’s contrast test when initial ANOVA indicated statistical differences between experimental groups. Mann-Whitney analyses were used to compare differences between groups (control and exercise groups) at baseline. Results: The expression of ACE mRNA in week 4 and in the Ex group was significantly lower than in the Con group (P Conclusions: These results suggest that moderate intensity exercise promotes the leukocyte expression of gene markers that may affect blood pressure and decreases blood pressure by improving cardiovascular fitness levels in middle-aged men

Soft-started Induction Motor Modeling and Heating Issues for Different Starting Profiles Using a Flux Linkage ABC Frame of Reference

In order to mitigate the adverse effects of starting torque transients and high inrush currents in induction motors, a popular method is to use electronically controlled soft-starting voltages utilizing series-connected silicon-controlled rectifiers (SCRs). Investigation of semioptimum soft-starting voltage profiles was implemented using a flux linkage ABC frame of reference model of a soft-started three-phase induction motor. A state-space model of the soft-starter thyristor switching sequence for the motor and load was developed and implemented in a time-domain simulation to examine winding heating and shaft stress issues for different starting profiles. Simulation results of line starts and soft starts were compared with measured data through which validation of the model was established. In this paper, different induction machine soft-start profiles are shown, and comparisons of starting times, torque profiles, and heating losses are made. Discussion of these results and conclusions as to the near-optimum types of profiles are delineated based on peak torque, starting times, and winding heating criteri

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

ArticleInPressOne of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies

An investigation on the sturgeon stocks in southern Caspian Sea

We launched a summer and winter cruise using two fishing vessels to assess the stocks of the sturgeon fish in the southern coasts of the Caspian Sea in the year 2004-2005. Eighty five random stations were selected and sampled using a 9 meter trawl in waters less than 10 meters and a 24.7 meter trawl for the depths above 10 meters. We caught 170 sturgeons in the summer cruise which comprised 142 Acipenser persicus, 19 Acipenser stellatus, 4 Huso huso, 3 Acipenser nudiventris and 2 Acipenser guldenstadtti. In the winter cruise, 118 sturgeons were caught of which 75 were A. persicus, 41 were A. stellatus, 1 was A. nudiventris and 1 was A. guldenstadtti. Catch per unit effort (CPUE) was calculated as 2 fish for summer cruise with the A. persicus being the most abundant species with 1.67 individuals per trawling while for A. stellatus this was 0.22. For H. huso, the CPUE was 0.05 and for A. nudiventris it was 0.04. For winter cruise, the CPUE was calculated as 1.38 fish, again with the A. persicus as the most abundant with 0.88, while that of the A. stellatus was 0.48. The CPUE for A. guldenstadtti and A. nudiventris was 0.01 in the winter cruise

Stock assessment of juvenile sturgeons in the Iranian water of the Caspian Sea by bottom trawl survey

The sturgeon stock assessment was performed to aim at estimation of absolute and relative abundance and determination of species composition at lower 10 m depths using the Si-Sara2 RV vessel in the Iranian coasts of the Caspian Sea in Guilan, Mazandaran and Golestan provinces during 6-30 September 2011-2012. In this study, 40 stations were selected on the basis of stratified random sampling design and then the stock estimation was performed using the swept area method. The study was carried out using bottom trawling with 9 m head rope. The time and speed of trawling in each station were 30 minutes and 2.5 knots respectively. The Catch per Unit of Effort (CPUE) in 2011–2012 were 7.03 and 6.96 individuals per trawling, respectively. The catch per unit of area in these years were found to be 1662 and 1644 fish in nm2, respectively.Total abundance of sturgeon juveniles was 13,327,164 individuals in 2011. So, the species composition included A. persicus (87.8%) and A. stellatus (12.2%). Total abundance of sturgeon juveniles was found to be 14,364,882 individuals in 2012 and the species composition comprised A. persicus (61.4%) and A. stellatus (38.6%). In 2011 the biomass of sturgeons in Iranian coastal water of the Caspian Sea was 295 tons and the composition of biomass included A. persicus (81.5%) and A. stellatus (18.5%), respectively. In the cruises conducted in 2011, this amount was estimated to be 217 tons comprising A. persicus (54.2%) and A. stellatus (45.8%), respectively. The results of this study in 2011–2012 showed remarkable abundance of juvenile sturgeons in Iranian coastal waters of the Caspian Sea in late summer and early autumn. So, by conserving these valuable stocks, the number of spawners will be increased in the future

FREE INTERACTOR MATRIX METHOD FOR CONTROL PERFORMANCE ASSESSMENT OF MULTI-VARIATE SYSTEMS

In this paper, an alternative method for the assessment of multi-vitiate control loop performance with consider twocircumstances. First, known time delays between each pair of inputs and outputs, and second, without relying on any a priori knowledge about the process model or timedelays. The performance of the control loop is calculated from data driven autoregressive moving average (ARMA) and prediction error model. It is clear that the limited data in scalar measure used for performance assessment results tends to steady-state as time tends to infinity, but large number of samples gives risen in scalar measures and tends to infinity as time samples tends to infinity and therefore it becomes difficult to calculate the performance index. In this paper, the later problem is solved by considering initial part of scalar measures with steady value for next-to-next time samples to calculate the control-loop performance index which would be utilized to decide healthy working of the control loop. Simulation example is included to show the performance index of multi-variate control loop

About Gravitomagnetism

The gravitomagnetic field is the force exerted by a moving body on the basis of the intriguing interplay between geometry and dynamics which is the analog to the magnetic field of a moving charged body in electromagnetism. The existence of such a field has been demonstrated based on special relativity approach and also by special relativity plus the gravitational time dilation for two different cases, a moving infinite line and a uniformly moving point mass, respectively. We treat these two approaches when the applied cases are switched while appropriate key points are employed. Thus, we demonstrate that the strength of the resulted gravitomagnetic field in the latter approach is twice the former. Then, we also discuss the full linearized general relativity and show that it should give the same strength for gravitomagnetic field as the latter approach. Hence, through an exact analogy with the electrodynamic equations, we present an argument in order to indicate the best definition amongst those considered in this issue in the literature. Finally, we investigate the gravitomagnetic effects and consequences of different definitions on the geodesic equation including the second order approximation terms.Comment: 16 pages, a few amendments have been performed and a new section has been adde

Training, Self-Efficacy, and Performance; a Replication Study

A conceptual replication of multiple prior IS studies was conducted with the aim of providing stronger empirical support for those results. Conducting six separate longitudinal studies, the effect of professional training on improving one’s application-specific computer self-efficacy (AS-CSE) was shown. Also in line with some prior IS studies it was shown that an application-specific measure of self-efficacy is better able to predict one’s performance in accomplishing tasks in the corresponding domain than a general computer self-efficacy (GCSE) measure. Moreover, it is shown that, regardless of the type and characteristics of the training method, individuals’ perceptions of quality of training significantly affects their AS-CSE after the training course