Social representations of HIV/AIDS in five Central European and Eastern European countries: A multidimensional analysis

Cognitive processing models of risky sexual behaviour have proliferated in the two decades since the first reporting of HIV/AIDS, but far less attention has been paid to individual and group representations of the epidemic and the relationship between these representations and reported sexual behaviours. In this study, 494 business people and medics from Estonia, Georgia, Hungary, Poland and Russia sorted free associations around HIV/AIDS in a matrix completion task. Exploratory factor and multidimensional scaling analyses revealed two main dimensions (labelled ‘Sex’ and ‘Deadly disease’), with significant cultural and gender variations along both dimension scores. Possible explanations for these results are discussed in the light of growing concerns over the spread of the epidemic in this region

Values of sexual behaviour in Central and Eastern Europe

Despite the profusion of social cognitive models for the prediction of sexual behaviour, we have only limited knowledge as to the role of individual values in predicting risky sexual activity. This study assessed the relationship between a recently developed value structure and sexual behaviour in the context of rising HIV infection in central and eastern Europe. Five hundred and three respondents (business people, doctors and nurses) from Estonia, Georgia, Hungary, Poland and Russia completed Schwartz’s Portrait Values Questionnaire and reported their condom use, partnership history and record of sexual disease. Results indicated that values had a moderate but consistent relationship with sexual behaviour, with riskier sexual activity reported by those high on Openness to Change, Hedonism and Self-Enhancement. These findings are discussed in the context of the need for culturally sensitive interventions in order to tackle the growing HIV epidemic in this region.This project was supported by a research grant from the Research Support Scheme operated by the Soros Foundation, Prague

Simple Controlling Ecofriendly Synthesis of Silver Nanoparticles at Room Temperature Using Lemon Juice Extract and Commercial Rice Vinegar

Silver nanoparticles were prepared in an ecofriendly manner at room temperature via the stepwise-modified Tollens route using the lemon juice extract and commercial rice vinegar. In this work, the lemon juice extract—a natural-origin chemical—was used as a reducing and stabilizing agent, and commercial rice vinegar was used to create a low acidic environment to control the silver nanoparticle growth via the stepwise method. The average dimension of silver nanoparticles was qualitatively evaluated through the UV-Vis spectra via the Mie theory. The X-ray diffraction and field emission scanning electron spectroscopy were employed to study the purity, the crystal structure, and the morphology of samples, respectively. Due to the weak activity and low purity of ecofriendly chemicals, the reaction and baking times strongly affect the preparation efficiency in obtaining small-size silver nanoparticles (∼40 nm). The highest efficiency was obtained with 24 h reaction time and 48 h baking time. The bimodal distribution of the size of silver nanoparticles was observed by UV-Vis analysis and field emission scanning electron microscopy. The obtained small-size silver nanoparticles (∼40 nm) have a uniform dimension. The quality of the obtained silver nanoparticles was evaluated through the conducting properties of silver paint made from ecosynthesized silver nanoparticles which showed a promising prospect to develop green-synthesized silver paint working at room temperature

Hard Exudates Segmentation in Fundus Image via Combining Automatic and CNN-based Interactive Methods

Hard exudates (EX) are one of the early signs of the diabetic retinopathy (DR), a leading cause of irreversible blindness. Computer assisted segmentation method makes screening faster and provide quantitative measurements of EX lesions. In this paper, we propose a method which takes advantage of both an automatic segmentation model and an interactive one which enables click-based corrections in the case of supoptimal EX segmentation. We evaluated the proposed method on three fundus image datasets including two public datasets (IDRiD and DDR) and a private dataset (E-Hos) using the area under precision-recall curve (AUPR) metric. The results show that the proposed method achieved AUPR scores of 0.851, 0.693 and 0.761 on the IDRiD, DDR and E-Hos datasets, respectively. The experiments also demonstrates that the proposed method outperforms several state-of-the-art CNNs in EX lesion segmentation