[Premature membrane rupture. Comparison of diagnostic tests].

BACKGROUND: Objective of this study was to evaluate the accuracy of the vaginal pH-test, the Fern-test, the research of foetal cells and of foetal fibronectin in vaginal discharge, which are used to diagnose premature rupture of membranes. METHODS: To this aim 40 pregnant patients between 24th and 37th weeks gestation have been examined, considered at risk for sub-clinic loss of aminiotic fluid: 23 were affected by preterm labour and 17 by suspected rupture of membranes. RESULTS: Subsequently amniotic sac was confirmed to be ripped in 10 cases (25%): 2 (8.7%) in the 23 patients with preterm labour, and 8 (47%) in the 17 patients with suspected PROM. Sensibility, specificity and accuracy were respectively: 70, 97 and 90% for pH-test; 70, 100 and 93% for Fern-test; 50, 93 and 82% for foetal cells; 100, 90 and 93% for fibronectin test. CONCLUSIONS: In personal experience fibronectin test appeared to be the most sensible and accurate marker. Fern-test was the most specific, while the research of foetal cells appeared to be the least reliable

Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification

Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent past. However, an objective comparison between published methods is difficult, since existing works use different datasets for evaluation and often compare against un-tuned baselines with default hyper-parameters. We design a benchmark for data-efficient image classification consisting of six diverse datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). Using this benchmark, we re-evaluate the standard cross-entropy baseline and eight methods for data-efficient deep learning published between 2017 and 2021 at renowned venues. For a fair and realistic comparison, we carefully tune the hyper-parameters of all methods on each dataset. Surprisingly, we find that tuning learning rate, weight decay, and batch size on a separate validation split results in a highly competitive baseline, which outperforms all but one specialized method and performs competitively to the remaining one

Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers\u2019 perceptions based on twitter data mining

This study explores if there is a convergence between the concepts of fashion and eco-friendliness in consumer perception of a fashion brand. We assume that increased eco-friendly perception will influence the brand image positively, with this impact being much higher for luxury than for high and fast fashion brands. The hypotheses are tested using data collected from Twitter. We analyzed the fashion clothing brands with the highest number of followers on the Socialbakers list and applied a novel social network mining methodology that allows measuring the relationship between each brand and two perceptual attributes (fashion and eco-friendliness). The method is based on attribute exemplars\u2014that is, Twitter accounts that represent a perceptual attribute. Our exemplars catalyze social media conversations on fashion (identified in our research by the keywords \u201cfashion,\u201d \u201cglamour,\u201d and \u201cstyle\u201d) and eco-friendliness (keywords \u201cenvironment\u201d and \u201cethical business\u201d). Based on social network analysis theory, we computed a similarity function between the followers of the exemplars and those of the brand. The results suggest that there is a correlation between the fashion and the eco-friendliness perceptual attributes of a brand; however, this correlation is far stronger for luxury brands than for high and fast fashion brands. The difference in the correlations confirms the recent tendency of fashion luxury brand to increasingly consider treating environmental issues as part of their core business and not just as added value to the brand's offer