Oxidative Stress and Parkinson's Disease: New Hopes in Treatment with Herbal Antioxidants

Parkinson's disease (PD) is a neurodegenerative disorder due to dopamine deficit in substatia nigra. PD is mainly a sporadic disease with unestablished etiology. However, exposure to environmental toxins, head trauma, inflammation, and free radicals are potential reasons. Recently, the role of oxidative stress in neurological abnormalities, including PD, has been particularly addressed. Antioxidant remedies, particularly herbal antioxidants, have revealed new perspectives of research and therapy as possible preventive and therapeutic approaches for PD. In this paper, we reviewed the recently published papers on the effects of herbal medicines on PD alongside the pathogenesis of PD with regard to oxidative stress

Normal Exophthalmometry Values in Iranian Population: A Meta-analysis: A complete translation from Farsi

This article is based on a study first reported in Farsi in the Bina Journal of Ophthalmology, titled بررسی مقادیر طبیعی اگزوفتالمومتري در جمعیت ایرانی: مطالعه مرور نظامند و متاآنالیز, Volume 24, Issue 2 (Winter 2019) 2019/05/28. Original URL: https://binajournal.org/article-1-985-fa.pdf There are limited studies on the normal values of eye protrusion in Iran. Systematic efforts to provide acceptable normal exophthalmometry values for Iranian population are required for a proper approach to orbital diseases. English and Farsi language publications in PubMed, the ISI Web of Knowledge database, Iranian SID, and Iran Medex were searched using the following keywords: “proptosis”, “eye protrusion”, “exophthalmous”, “Hertel exophthalmometer” and “Iran”. Four articles from 1995 to 2010 were found and included in the meta-analysis. Statistical analysis was performed using the Metan command within Stata 15.0 software. It included 3,696 subjects in whom the average eye protrusion was 16.5 mm (95% CI: 15.1–17.8) in men and 16.2 mm (95% CI: 14.6–17.7) in women (P = 0.5). Mean left and right eye protrusion were 16.3 (95% CI: 14.7–18.1) and 16.4 mm (95% CI: 14.8–17.7), (P = 0.3), respectively. While Iranian teenagers (13–19 years old) showed a mean value of 17.1 mm (95% CI: 15.0–19.1), older age group (≥20 years) showed a lower mean eye protrusion of 16.3 mm (95% CI: 14.8–17.7). Considering the two standard deviations, the highest normal value of eye protrusion in Iranian population is 20.1 mm. In conclusion, Iranian normal eye protrusion values were higher than Asians and lower than Caucasians

Return Volatility and Asymmetric News of Computer Industry stocks in Tehran Stock Exchange (TEX)

According to leverage and volatility feedback effects there are relationships between the return and the risk of stocks in the stock markets. Using daily and weekly data of Computer industry index in Tehran stock Exchange (TEX), this study investigates both leverage and volatility feedback effects applying GARCH family models and Full Information Maximum Likelihood (FIML) estimation method, during 01/2007- 10/2013 period. According to GARCH-M model estimations the first hypothesis of the research (Return volatility of computer industry in TEX affects the return significantly) cannot be rejected for daily data during 02/2010 to 10/2013 (the 2nd period) which both return and return volatility were much more volatile rather than 01/2007 - 02/2010 (the 1st period), but this hypothesis can be rejected for daily data in the 1st period and weekly data in both periods. According to EGARCH and TGARCH estimations the second main hypothesis of the research (a negative return makes return volatility of computer industry in TEX more volatile) cannot be rejected for both daily and weekly data in the 1st period, but can be rejected for both data during the 2nd period

Medication use in uncontrolled pediatric asthma:Results from the SysPharmPediA study

Background: Uncontrolled pediatric asthma has a large impact on patients and their caregivers. More insight into determinants of uncontrolled asthma is needed. We aim to compare treatment regimens, inhaler techniques, medication adherence and other characteristics of children with controlled and uncontrolled asthma in the: Systems Pharmacology approach to uncontrolled Paediatric Asthma (SysPharmPediA) study. Material and methods: 145 children with moderate to severe doctor-diagnosed asthma (91 uncontrolled and 54 controlled) aged 6–17 years were enrolled in this multicountry, (Germany, Slovenia, Spain, and the Netherlands) observational, case-control study. The definition of uncontrolled asthma was based on asthma symptoms and/or exacerbations in the past year. Patient-reported adherence and clinician-reported medication use were assessed, as well as lung function and inhalation technique. A logistic regression model was fitted to assess determinants of uncontrolled pediatric asthma. Results: Children in higher asthma treatment steps had a higher risk of uncontrolled asthma (OR (95%CI): 3.30 (1.56–7.19)). The risk of uncontrolled asthma was associated with a larger change in FEV1% predicted post and pre-salbutamol (OR (95%CI): 1.08 (1.02–1.15)). Adherence and inhaler techniques were not associated with risk of uncontrolled asthma in this population. Conclusion: This study showed that children with uncontrolled moderate-to-severe asthma were treated in higher treatment steps compared to their controlled peers, but still showed a higher reversibility response to salbutamol. Self-reported adherence and inhaler technique scores did not differ between controlled and uncontrolled asthmatic children. Other determinants, such as environmental factors and differences in biological profiles, may influence the risk of uncontrolled asthma in this moderate to severe asthmatic population

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Financial Development, International Trade and Economic Growth: Empirical Evidence from Pakistan

The study utilizes the Autoregressive-distributed lag (ARDL) approach for cointegration and Granger causality test, to explore the long run equilibrium relationship and the possible direction of causality between international trade, financial development and economic growth for the Pakistan economy. Imports plus exports of goods and services is used as a proxy for international trade, while broad money (M2) and gross domestic product (GDP) are used as the proxies for financial development and economic growth, respectively. Result explores a long run relationship between the variables. In case of Pakistan, economy supply leading hypothesis is accepted. Moreover, unidirectional causality is observed from international trade to economic growth and from financial development to international trade

Energy consumption prediction using machine learning: A review

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models

Scalable performance analysis of exascale MPI programs through signature-based clustering algorithms

Extreme-scale computing poses a number of challenges to application performance. Developers need to study appli-cation behavior by collecting detailed information with the help of tracing toolsets to determine shortcomings. But not only applications are“scalability challenged”, current tracing toolsets also fall short of exascale requirements for low back-ground overhead since trace collection for each execution en-tity is becoming infeasible. One effective solution is to clus-ter processes with the same behavior into groups. Instead of collecting performance information from each individual node, this information can be collected from just a set of representative nodes. This work contributes a fast, scalable, signature-based clustering algorithm that clusters processes exhibiting similar execution behavior. Instead of prior work based on statistical clustering, our approach produces pre-cise results nearly without loss of events or accuracy. The proposed algorithm combines low overhead at the clustering level with log(P) time complexity, and it splits the merge process to make tracing suitable for extreme-scale comput-ing. Overall, this multi-level precise clustering based on sig-natures further generalizes to a novel multi-metric clustering technique with unprecedented low overhead. Categories and Subject Descriptor