The effect of 12 weeks regular physical activity and vitamin E in the treatment of non-alcoholic steatohepatitis: A pilot study

Background: Despite the prevalence of Non-Alcoholic Fatty Liver Disease (NAFLD) and Non-Alcoholic Steatohepatitis (NASH), there was no treatment has been proven to be effective in these common diseases. Although many studies have shown that lifestyle modifications such as increasing physical activities and exercise could be effective in the treatment of these common diseases, the optimal strategy was still not determined. According to the beneficial effects of antioxidant agents in the treatment of NASH, vitamin E has been used for this purpose by some clinicians. We designed this study for assessing beneficial effects of regular physical activity on the biochemical and imaging responses in patients with NASH and comparing this with vitamin E as an accepted treatment for NASH. Materials and Methods: This study was Randomized and single-blind clinical trials were carried out in Gonbad-e Kavus through which a total of 30 consecutive patients with the ultra sonographic diagnosis of non-alcoholic steatohepatitis (NASH)were enrolled and randomized to one of the three groups: Vitamin E 800 mg/day, regular physical activity, or both. Results: In all treatment groups improvement in liver transaminases level, serum lipids and ultrasonographic grading of fatty liver occurred after three months of treatment. When these decrement was compared between the treatment groups, there was no statistically significant difference in the value of improvement between the three groups (ANOVA: p>0.5). I.e. all three interventions improved the biochemical and ultrasonographic finding of fatty liver in the same way. Both groups with regular exercise had significant mean weight loss in comparison with the vitamin E group (a mean decrease of 3.0 kg in exercise group, 5.8 kg in subjects on regular exercise plus vitamin E and 0.2 kg in vitamin E group, ANOVA: p=0.04). Conclusion: There were no significant differences between exercise and vitamin E alone or in combination regarding the reduction in the level of liver enzymes and sonographic evidences of fatty liver although both resulted in significant improvements in biochemical endpoints. This implies that physical activity could be considered as effective as vitamin E in the improvement of biochemical and ultrasonographic presentations of NASH and the addition of Vitamin E does not offer any benefits. According to the findings of this pilot study a full-powered study with a control group should be designed. © 2015, Iranian Association of Gastroenterology and Hepatology. All rights reserved

Properties of sub-diffraction limited focusing by optical phase conjugation

Recent work has demonstrated sub-diffraction limited focusing using time-reversal mirrors and sources in scattering media at microwave frequencies. We numerically investigate the possibility of observing analogous effects in the optical domain using small cylindrical scatterers of realistic dielectric materials combined with an enclosing optical phase conjugate mirror in two-dimensional geometries. Such focusing is possible but appears not to significantly exceed the focusing available from an equivalent homogenized material, and is highly sensitive to precise scatterer configuration. © 2010 Optical Society of America

Image Processing Failure and Deep Learning Success in Lawn Measurement

Lawn area measurement is an application of image processing and deep learning. Researchers have been used hierarchical networks, segmented images and many other methods to measure lawn area. Methods effectiveness and accuracy varies. In this project Image processing and deep learning methods has been compared to find the best way to measure the lawn area. Three Image processing methods using OpenCV has been compared to Convolutional Neural network which is one of the most famous and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional Neural Network or shortly CNN shows very high accuracy (94-97%) for this purpose. In image processing methods, Thresholding with 80-87% accuracy and Edge detection are effective methods to measure the lawn area but Contouring with 26-31% accuracy does not calculate the lawn area successfully. We may conclude that deep learning methods especially CNN could be the best detective method comparing to image processing and other deep learning techniques

Distribution, diversity and abundance of fish species in the Madarsoo River, Golestan National Park, Iran

To assess the effects of two flooding events occurred in the years 2001 and 2002, fish distribution, diversity and abundance in Madarsoo River of the Golestan National Park were studied and compared to that of the years before the events. A total of five fish species from four sites were collected. Assemblage of fish population showed changes in their overall relative abundance and distribution across sampling sites and times. We estimated the abundance of Capoeta capoeta gracilis at 2.331:2.6, Alburnoides bipunctatus at 0.17±0.15, Paracobitis malapterura at 0.0474±0.031 and Leuciscus cephalus at 0.005±0.01 fish per square meter of the stream. The abundance of Cc.gracilis showed significant increase while that of the A. bipunctatus did not undergo such significant change in comparison with the data from the years before flooding events (P<0.01). The Shannon diversity index was significantly different between sites and with the increase in the number of riffles and pools in the river, the index and population size of the fish species showed an upward trend. Three fish species Oncorhynchus mykiss, Neogobius melanostomus affinis and Neogobius fluviafilis which were abundant before the floods did not show up in the samples at all. Also, of the Barbus mursa, only one specimen was caught. Canonical correspondence analysis (CCA) results suggest that two species C. c. gracilis and P. malapterura are more resistant against changes in environmental conditions

Dynamics of single polymers under extreme confinement

We study the dynamics of a single chain polymer confined to a two dimensional cell. We introduce a kinetically constrained lattice gas model that preserves the connectivity of the chain, and we use this kinetically constrained model to study the dynamics of the polymer at varying densities through Monte Carlo simulations. Even at densities close to the fully-packed configuration, we find that the monomers comprising the chain manage to diffuse around the box with a root mean square displacement of the order of the box dimensions over time scales for which the overall geometry of the polymer is, nevertheless, largely preserved. To capture this shape persistence, we define the local tangent field and study the two-time tangent-tangent correlation function, which exhibits a glass-like behavior. In both closed and open chains, we observe reptational motion and reshaping through local fingering events which entail global monomer displacement.Comment: 22 pages, 18 figures, slightly extended version to appear in JSTA

Effect of the nursing process using via "accessible care cards" on the patients' satisfaction of nursing care in intensive care units of Golestan Hospital, Ahvaz

Background and aims: Nursing process is performance Standard for nursing cares. According to their conditions and facilities, various centers can choose and implement a nursing process that is more efficient for them. This study was aimed to determine the effect of nursing process the way "accessible care cards" on patients' satisfaction from care in intensive care units. Methods: This is a cross-sectional interventional study with plan case-control study that was conducted in Golestan hospital in Ahwaz in 2014. Considering the inclusion criteria and available sampling method, a total of 38 controls and 38 cases were selected for the intervention group were evaluated. Data were collected questionnaire made by researcher, which was assess the scientific validity, the content validity method were used validated. As well as its reliability using Cronbach's alpha test (α= 0.9) was determined. Intervention was conducted by accessible care cards in nursing process for two weeks and its impact on patient satisfaction was measured. The control group was routine care. The collected data was analyzed by SPSS 19 and statistical tests. Descriptive statistics such as mean, and standard deviation was used. Chi-square test, Mann-whitney and t-test were used to compare groups. Results: Results showed that 28.90% of patients in the control group had great satisfaction of care, while the 97.36% of patients in the intervention group reported nearly excellent satisfaction. The observed difference in patients' satisfaction with nursing care in both the experimental and control group was statistically significant (P<0.001). Conclusion: Implementation of nursing process, in a manner of available cards led to an increase in patient satisfaction compared with the routine manner and the use of these cards makes the nursing process of a greater degree of mental state, to objectively state, that can help in saving a lot of time for nurses

Human action recognition via skeletal and depth based feature fusion

This paper addresses the problem of recognizing human actions captured with depth cameras. Human action recognition is a challenging task as the articulated action data is high dimensional in both spatial and temporal domains. An effective approach to handle this complexity is to divide human body into different body parts according to human skeletal joint positions, and performs recognition based on these part-based feature descriptors. Since different types of features could share some similar hidden structures, and different actions may be well characterized by properties common to all features (sharable structure) and those specific to a feature (specific structure), we propose a joint group sparse regression-based learning method to model each action. Our method can mine the sharable and specific structures among its part-based multiple features meanwhile imposing the importance of these part-based feature structures by joint group sparse regularization, in favor of discriminative part-based feature structure selection. To represent the dynamics and appearance of the human body parts, we employ part-based multiple features extracted from skeleton and depth data respectively. Then, using the group sparse regularization techniques, we have derived an algorithm for mining the key part-based features in the proposed learning framework. The resulting features derived from the learnt weight matrices are more discriminative for multi-task classification. Through extensive experiments on three public datasets, we demonstrate that our approach outperforms existing methods