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  • Индикация изменений климата на севере Европейской территории России по материалам фенологических наблюдений
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Subject
News
June 22, 2026
‘In Science, You Are Your Own Boss
Polina Nasledskova is interested in identifying gaps in linguistics and topics that have been overlooked by other researchers. In an interview for the  Young Scientists of HSE University project, she spoke about rare ordinal numerals in Nakh-Daghestanian languages, the benefits of knitting for concentration, and the beauty of the Patriarshy Bridge.
June 19, 2026
HSE Researchers Determine Which Internet Users Are More Likely to Fact-Check
Researchers at HSE University examined the strategies employed by Russian internet users to verify unreliable information and the factors that motivate them to do so. The study found that more than half of users who encounter potentially false information online attempt to verify it by locating the original source. The likelihood of fact-checking is influenced by several factors, including age, place of residence, social status, information literacy skills, and the use of AI. The findings have been published in Monitoring of Public Opinion: Economic and Social Changes.
June 5, 2026
'Im Used to Producing Distilled Knowledge'
Ivan Rubachev works in a HSE University laboratory established jointly with Yandex Research, where he focuses on machine learning with tabular data. In this interview with the HSE Young Scientists project, he discusses why following a vibe can be better than goal-setting, explains the concept of the Neural Turing Machine, and argues why withholding scientific knowledge is counterproductive.

 

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Индикация изменений климата на севере Европейской территории России по материалам фенологических наблюдений

На пути к устойчивому развитию России. 2015. № 71. С. 49–52.
Minin A. A., Воскова А. В.

The problems of Climate Change and phenology are discussed

Research target: Earth Sciences Biology
Priority areas: economics
Language: Russian
Full text
Keywords: изменения климатаphenologyфенология
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Drosophila homeotic gene Abdominal-B (Abd-B) is controlled throughout development by four infraabdominal (iab) regulatory domains, the active or repressed state of which is determined by initiators that have parasegment-specific enhancer activity at an early stage of embryonic development. For this reason, it has long been assumed that the enhancer activity and initiation function of these elements are synonymous. ...
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The specification of abdominal segments A5 to A9 depends on the expression of Abdominal-B (Abd-B), which is regulated by four infraabdominal domains: iab-5 through iab-8,9. Each iab domain contains an initiation element that determines its active state, along with enhancers responsible for tissue-specific activation of Abd-B. These iab domains function autonomously due to their flanking boundaries, Fab-6, Fab-7 and Fab-8, which both block crosstalk between adjacent iab domains (insulator function) and facilitate long-range ...
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β-Bungarotoxins (β-BuTx), consisting of covalently bound phospholipase A2 subunit (A-chain), a member of group Ia of secretory phospholipases A2, and non-enzymatic subunit (B-chain) structurally related to Kunitz-type protease inhibitors, block presynaptic neuromuscular transmission via a not completely defined mechanism of action. In vivo physiological studies revealed that the B-chain is targeting voltage-gated potassium channels of not identified subtypes. In our work, six β-BuTx isoforms were ...
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Benchmarking DNA large language models on quadruplexes
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Large language models (LLMs) in genomics have successfully predicted various functional genomic elements. While their performance is typically evaluated using genomic benchmark datasets, it remains unclear which LLM is best suited for specific downstream tasks, particularly for generating whole-genome annotations. Current LLMs in genomics fall into three main categories: transformer-based models, long convolution-based models, and state-space models ...
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Kolmogorov–Arnold networks for genomic tasks
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Kolmogorov–Arnold networks (KANs) emerged as a promising alternative for multilayer perceptrons (MLPs) in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures in the domains of computer vision and natural language processing. Integrating KANs into deep learning models for genomic tasks has not been explored. Here, we ...
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Molecular biology of the deadliest cancer – glioblastoma: what do we know?
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