The influence of calcium and magnesium ions on resistance to dehydration in the yeast, Saccharomyces cerevisiae, was investigated. Magnesium ion availability directly influenced yeast cells' resistance to dehydration and, when additionally supplemented with calcium ions, this provided further significant increase of yeast resistance to dehydration. Gradual rehydration of dry yeast cells in water vapour indicated that both magnesium and calcium may be important for the stabilization of yeast cell membranes. In particular, calcium ions were shown for the first time to increase the resistance of yeast cells to dehydration in stress-sensitive cultures from exponential growth phases. It is concluded that magnesium and calcium ion supplementations in nutrient media may increase the dehydration stress tolerance of S. cerevisiae cells significantly, and this finding is important for the production of active dry yeast preparations for food and fermentation industries.
We study the effectiveness of contextualized embeddings for the task of diachronic semantic change detection for Russian language data. Evaluation test sets consist of Russian nouns and adjectives annotated based on their occurrences in texts created in pre-Soviet, Soviet and post-Soviet time periods. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their semantic change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.
We study the effectiveness of contextualized embeddings for the task of diachronic semantic change detection for Russian language data. Evaluation test sets consist of Russian nouns and adjectives annotated based on their occurrences in texts created in pre-Soviet, Soviet and post-Soviet time periods. ELMo and BERT architectures are compared on the task of ranking Russian words according to the degree of their semantic change over time. We use several methods for aggregation of contextualized embeddings from these architectures and evaluate their performance. Finally, we compare unsupervised and supervised techniques in this task.
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