Sarcopenia

Exposure to air pollutants has been previously associated with respiratory viral infections, including influenza, measles, mumps, rhinovirus, and respiratory syncytial virus. Epidemiological studies have also suggested that air pollution exposure is associated with increased cases of SARS-CoV-2 infection and COVID-19-associated mortality, although the molecular mechanisms by which pollutant exposure affects viral infection and pathogenesis of COVID-19 remain unknown. In this review, we suggest potential molecular mechanisms that could account for this association. We have focused on the potential effect of exposure to nitrogen dioxide (NO2 ), ozone (O3 ), and particulate matter (PM) since there are studies investigating how exposure to these pollutants affects the life cycle of other viruses. We have concluded that pollutant exposure may affect different stages of the viral life cycle, including inhibition of mucociliary clearance, alteration of viral receptors and proteases required for entry, changes to antiviral interferon production and viral replication, changes in viral assembly mediated by autophagy, prevention of uptake by macrophages, and promotion of viral spread by increasing epithelial permeability. We believe that exposure to pollutants skews adaptive immune responses toward bacterial/allergic immune responses, as opposed to antiviral responses. Exposure to air pollutants could also predispose exposed populations toward developing COIVD-19-associated immunopathology, enhancing virus-induced tissue inflammation and damage.

https://nyaspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/nyas.14512

SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: What is the connection?

Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming. Principles and Modeling Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields. Formalism and Algorithms. The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems. FAQsAlong with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability. The First Steps toward a Bayesian ComputerA new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.

Bayesian Programming

Despite the virulence and high fatality of coronavirus disease 2019 (COVID-19), no specific antiviral treatment exists until the current moment. Natural agents with immune-promoting potentials such as bee products are being explored as possible treatments. Bee honey and propolis are rich in bioactive compounds that express strong antimicrobial, bactericidal, antiviral, anti-inflammatory, immunomodulatory, and antioxidant activities. This review examined the literature for the anti-COVID-19 effects of bee honey and propolis, with the aim of optimizing the use of these handy products as prophylactic or adjuvant treatments for people infected with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Molecular simulations show that flavonoids in propolis and honey (e.g., rutin, naringin, caffeic acid phenyl ester, luteolin, and artepillin C) may inhibit viral spike fusion in host cells, viral-host interactions that trigger the cytokine storm, and viral replication. Similar to the potent antiviral drug remdesivir, rutin, propolis ethanolic extract, and propolis liposomes inhibited non-structural proteins of SARS-CoV-2 in vitro, and these compounds along with naringin inhibited SARS-CoV-2 infection in Vero E6 cells. Propolis extracts delivered by nanocarriers exhibit better antiviral effects against SARS-CoV-2 than ethanolic extracts. In line, hospitalized COVID-19 patients receiving green Brazilian propolis or a combination of honey and Nigella sativa exhibited earlier viral clearance, symptom recovery, discharge from the hospital as well as less mortality than counterparts receiving standard care alone. Thus, the use of bee products as an adjuvant treatment for COVID-19 may produce beneficial effects. Implications for treatment outcomes and issues to be considered in future studies are discussed.

Propolis, Bee Honey, and Their Components Protect against Coronavirus Disease 2019 (COVID-19): A Review of In Silico, In Vitro, and Clinical Studies.

In this paper we examine the behavior of Ad Hoc networks through simulations, using different routing protocols and various topologies. We examine the difference in performance, using CBR application, with packets of different size through a variety of topologies, showing the impact node placement has on networks performance. We show that the choice of routing protocol plays an important role on network’s performance. We also quantify node mobility effects, by looking into both static and fully mobile configurations. Our paper presents a systematic analysis of a variety of different ad hoc network topologies in terms of node placement, node mobility and routing protocols through several simulated scenarios.

IJCSI Publicity Board 2010

Menopause is an inevitable developmental event that women encounter at an age of 42-54 years. The drop of estrogen levels that accompanies cessation of menstruation is associated with multiple vasomotor, physical, neuropsychological, and sexual symptoms, which may hamper quality of life. This study aimed to examine the severity of psychological symptoms and their correlates among peri- and postmenopausal Emirati women (N = 60, mean age = 54.88 ± 6 years). Participants were interviewed using the Menopause-Specific Quality of Life (MENQOL) and attitudes toward menopause scale (ATMS). In four path analysis models, vasomotor symptoms, weight gain, and fatigue had significant direct effects on symptoms of anxiety, depression (only weight gain and fatigue), and psychological distress. Fatigue significantly mediated the effects of vasomotor symptoms and weight gain on symptoms of anxiety, depression (only vasomotor symptoms), psychological distress, and memory problems. These models explained 47.6%, 44.5%, 56.6%, and 29.1% of the variances in anxiety, depression, psychological distress, and memory problems, respectively. Participants with more severe menopausal symptoms expressed more negative attitudes toward menopause though regression analysis revealed that only vasomotor symptoms could significantly contribute to ATMS scores. In conclusion, psychological distress is widespread among menopausal women, and it is associated with vasomotor symptoms, fatigue, and change of body composition (obesity). Psychological symptoms, along with vasomotor symptoms, express a key link to negative attitudes toward menopause. Therefore, interventional strategies that target psychological distress may promote coping with midlife transition and improve mental health among menopausal women.

/pdf/psychological-climacteric-symptoms-and-attitudes-toward-32ilqxvf6i.pdf

Psychological Climacteric Symptoms and Attitudes toward Menopause among Emirati Women

To investigate the quality of life (QOL) of menopausal Emirati women aged 40–64 years and determine its relationship with their sociodemographic characteristics. A community-based cross-sectional study was conducted on 70 Emirati women using multistage stratified clustered random sampling. The participants were interviewed face-to-face using a structured questionnaire comprising sociodemographic variables, reproductive characteristics, and the Menopause-Specific Quality of Life (MENQOL) questionnaire. The most common symptom among the study participants was ‘aching in the muscles’. The participants had a moderate level of bothersome symptoms; in addition, vasomotor symptoms were reported by 61%, while sexual symptoms were only reported by one-third of the participants. There were no significant differences between the menopausal status in any of the four domains of the MENQOL questionnaire. Additionally, there were no significant differences between the mean scores of the four MENQOL domains and all predictors. This study highlights the importance of educating women about menopause and its symptoms.

/pdf/menopause-specific-quality-of-life-among-emirati-women-3e15dul872.pdf

Menopause-Specific Quality of Life among Emirati Women

We present a unifying framework for exact and approximate inference in Bayesian networks. This framework is used in "ProBT", a general purpose inference engine for probabilistic reasoning and incremental model construction. This paper is not intended to present ProB T but to describe its underlying algorithms mainly the "Successive Restrictions Algorithm " (SRA) for exact inference, and the "Monte Carlo Simultaneous Estimation and Maximization" (MCSEM) algorithm for approximate inference problems. The main idea of ProBT is to use "probability expressions " that can be "exact" or "approximate" as basic bricks to build more complex models incrementally.

Exact and approximate inference in ProBT

University students experience high levels of stress due to university transition, academic commitments, and financial matters Higher stress perceptions along with limited coping resources endanger mental health for a considerable number of students and may ruin their performance The current study evaluated the psychometric properties of the Cohen Perceived Stress Scale (10 items), PSS-10, in a sample of 379 female Emeriti students Exploratory factor analysis resulted in two factors with eigenvalues of 388 and 119, which explained 606% of the variance Confirmatory factor analysis revealed good model fits of two correlated factors (Comparative Fit Index (CFI) = 0962, Tucker-Lewis Index (TLI) = 0950, standardized root-mean-square residual (SRMR) = 00479, and root mean-square error of approximation (RMSEA) = 0067) Internal consistency of the PSS-10 and its positive and negative subscales was acceptable (coefficient α = 067, 079, and 086, respectively) Multigroup analysis revealed that the PSS-10 holds invariance across different groups of age, marital status, and financial status (average monthly expenditure) Convergent and concurrent validity tests signify the importance of considering scores of subscales of the PSS-10 along with its total score

/pdf/the-arabic-version-of-the-cohen-perceived-stress-scale-1seg8de19l.pdf

The Arabic Version of the Cohen Perceived Stress Scale: Factorial Validity and Measurement Invariance.

In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively.

Linda Smail

Papers

Psychological Climacteric Symptoms and Attitudes toward Menopause among Emirati Women

Menopause-Specific Quality of Life among Emirati Women

Exact and approximate inference in ProBT

The Arabic Version of the Cohen Perceived Stress Scale: Factorial Validity and Measurement Invariance.

Stock price forecast with deep learning