“
“Scholars argue about whether age stereotypes (beliefs about old people) are becoming more negative or positive over time. No previous study has systematically tested the trend of age stereotypes over more than 20 years, due to lack of suitable data. Our aim was to fill this gap by investigating whether age stereotypes selleck inhibitor have changed over the last two centuries and, if so,

what may be associated with this change. We hypothesized that age stereotypes have increased in negativity due, in part, to the increasing medicalization of aging. This study applied computational linguistics to the recently compiled Corpus of Historical American English (COHA), a database of 400 million words that includes a range of printed sources from 1810 to 2009. After generating a comprehensive list of synonyms for the term elderly for these years from two historical thesauri,

we identified 100 collocates (words that co-occurred most frequently with these synonyms) for each of the 20 decades. Inclusion criteria for the collocates were: (1) appeared within four words of the elderly synonym, (2) referred to an old person, and (3) had a stronger association with the elderly synonym than other words appearing in the database for that decade. This yielded 13,100 collocates that were rated for negativity and medicalization. We found that age stereotypes have become more negative in a linear way over 200 years. In 1880, age stereotypes switched from being positive to being negative. In addition, support was found for two potential explanations. Medicalization of aging see more and the growing proportion of the population over the age of 65 were both significantly associated with the increase in negative age stereotypes. The upward trajectory of age-stereotype negativity makes a case for remedial action on a societal level.”
“Purpose Comparison Liproxstatin-1 chemical structure of optical coherence tomography (OCT) segmentation performance regarding technical accuracy and clinical relevance.

Methods 29 eyes were imaged prospectively with Spectralis (Sp), Cirrus (Ci), 3D-OCT 2000 (3D) and RS-3000 (RS) OCTs. Raw data were evaluated in validated custom software. A 1 mm diameter subfield, centred on the fovea, was investigated to compare identical regions for each case. Segmentation errors were corrected on each B-scan enclosed in this subfield. Proportions of wrongly segmented A-scans were noted for inner and outer retinal boundaries. Centre point thickness (CPT) and central macular thickness (CMT) were compared before and after correction. Results Segmentation errors occurred in 77% and affected on average 29% of A-scans, resulting in mean differences of 24/13 mu m (CPT/CMT). The incidence of segmentation errors was 48% (Sp), 79% (Ci), 86% (3D) and 93% (RS), p smaller than 0.001.