A new family of synthetic diterpenes that regulates cytokine synthesis by inhibiting IkappaBalpha phosphorylation

The synthesis and the biological evaluation of a new family diterpenes are presented. The synthetic studies were inspired by the structural framework of acanthoic acid (1) and yielded a family of compounds that were evaluated as anti-inflammatory agents. Among them, compounds 2, 10, 12, and 16 exhibited a very low nonspecific cytotoxicity and inhibited the synthesis of TNF-alpha with greater than 65 % efficacy at low micromolar concentrations. Cytokine-specificity studies revealed that these compounds also inhibited the synthesis of the proinflammatory cytokines IL-1beta and IL-6, while inhibition of IL-1ra and IL-8 synthesis was marginal and only occurred at high concentrations. Further studies, through EMSA and Western blot analyses, indicated that these compounds decreased the extent of phosphorylation of IkappaBalpha; this suggests that they exert their anti-inflammatory profile by inhibiting NF-kappaB-mediated cytokine synthesis. These findings imply that these diterpenes represent promising leads for the development of novel anti-inflammatory agents.     

Synthesis,surface profile,nonlinear reflective index and photophysical properties of curcumin compound

Curcumin and other three curcuminoids (bisdemethoxycurcumin, α-chlorocurcumin and α-methylcurcumin) were synthesized. Fourier transform infrared spectroscopy, Fluorescence quantum yields, AFM analysis and image surface profiles were characterized. All compounds possessed electron donor moieties at both ends of the conjugated π-system and an electron acceptor moiety in the middle of the molecules (D-A-D system) and should exhibit different optical properties depending on substituents on the benzene rings. The third order nonlinear optical properties of the curcuminoids have been investigated by z-scan technique. The optical response was characterized by measuring the refractive index (n₂) of the derivatives of curcumin using the Z-scan technique. The compounds showed negative and large nonlinear refractive index values of the order of 10^?7 cm²/W and reverse saturable absorption with high values of the nonlinear absorption coefficient of the order of 10^?4 cm/W. The nonlinear refractive index was found to vary with the different compound. The optical constants of the different compound films were studied and the dispersion of the refractive index was discussed in terms of the Wemple-DiDomenico single oscillator model. The photo-physical properties of these compounds are compared to those of native curcumin, in order to provide a rationale to the design of samples with molecular structures optimized for a photosensitizer. These types of materials may be considering new photonic applications.     

Magnetic Field-Assisted Laser Ablation of Titanium Dioxide Nanoparticles in Water for Anti-Bacterial Applications

Journal of Inorganic and Organometallic Polymers and Materials - Titanium oxide nanoparticles (TiO2) were produced by pulsed Nd:YAG laser ablation in water under the effect of an external magnetic...     

Comparison of subject-specific and population averaged models for count data from cluster-unit intervention trials

Maximum likelihood estimation techniques for subject-specific (SS) generalized linear mixed models and generalized estimating equations for marginal or population-averaged (PA) models are often used for the analysis of cluster-unit intervention trials. Although both classes of procedures account for the presence of within-cluster correlations, the interpretations of fixed effects including intervention effect parameters differ in SS and PA models. Furthermore, closed-form mathematical expressions relating SS and PA parameters from the two respective approaches are generally lacking. This paper investigates the special case of correlated Poisson responses where, for a log-linear model with normal random effects, exact relationships are available. Equivalent PA model representations of two SS models commonly used in the analysis of nested cross-sectional cluster trials with count data are derived. The mathematical results are illustrated with count data from a large non-randomized cluster trial to reduce underage drinking. Knowledge of relationships among parameters in the respective mean and covariance models is essential to understanding empirical comparisons of the two approaches.     

Using semantic context for multiple concepts detection in still images

Pattern Analysis and Applications - Multimedia documents indexing systems performances have been improved significantly in recent years, especially after the involvement of deep learning...     

Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic, caused by Coronavirus Disease 2019 (COVID-19), has affected the daily activities of people across the globe. During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously. Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19. Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data. The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made upon COVID-19. It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors. For identification and classification of the sentiments, the Support Vector Machine (SVM) model is exploited. At last, the ABC algorithm is applied to fine tune the parameters involved in SVM. To demonstrate the improved performance of the proposed ABCML-SA model, a sequence of simulations was conducted. The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.     

ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions

This article describes a new software for modeling correlated binary data based on orthogonalized residuals, a recently developed estimating equations approach that includes, as a special case, alternating logistic regressions. The software is flexible with respect to fitting in that the user can choose estimating equations for association models based on alternating logistic regressions or orthogonalized residuals, the latter choice providing a non-diagonal working covariance matrix for second moment parameters providing potentially greater efficiency. Regression diagnostics based on this method are also implemented in the software. The mathematical background is briefly reviewed and the software is applied to medical data sets.