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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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ИССЛЕДОВАНИЕ АССОЦИАЦИИ ГЕНЕТИЧЕСКИХ ВАРИАНТОВ С РАЗВИТИЕМ МУЗЫКАЛЬНЫХ СПОСОБНОСТЕЙ ЧЕЛОВЕКА

ВАВИЛОВСКИЙ ЖУРНАЛ ГЕНЕТИКИ И СЕЛЕКЦИИ, Федеральный исследовательский центр Институт цитологии и генетики Сибирского отделения Российской академии наук» (ИЦиГ СО РАН) (Новосибирск). 2025. Vol. 30. No. 3. P. 470–481.
Kazantseva A. V., A.V. Toropova, Khusnutdinova E. K., Malykh S. B.

The development of musical abilities, including absolute pitch, musical memory, rhythm sense, and musicality, at a high degree is determined by a hereditary component (up to 68 %). The studies implementing a genome-wide linkage and association approach to musical aptitude have revealed more than 100 genetic loci. This spectrum is comprised of the genes encoding for transcription factors and those responsible for neurogenesis and synaptic plasticity, genes fixed as a result of positive selection of musicality, and those related to inner ear formation. Since no studies linking musical aptitude with genes have been previously conducted in Russia, the present study aimed at replicating the association of 17 previously identified genetic variants with developing musical abilities in Russians. Genotyping of SNPs in the GATA2, PCDH7, UNC5C, ASAP1, SBSPON, DCBLD2, KALRN, VLDLR, OTOF, GRIN2B, FoxP1, FoxP2, BDNF, EGR1, and SNCA genes was performed using competitive allele-specific PCR in a sample of students who underwent rigorous contest selection at admission to the conservatory and in the corresponding control group. A series of logistic regression analyses were used both to evaluate the main effect of SNP and to identify the best prognostic model based on various loci. The mathematical model obtained by including only statistically significant SNPs consisted of GATA2 rs9854612, SNCA rs356168, rs3910105, ASAP1 rs3057, and VLDLR rs1454626 (р = 0.0018, pseudo r2 = 0.188, AUC = 0.791). The addition of all examined SNPs as predictors enabled the construction of a statistically significant model with a higher predictive ability (р = 0.012, pseudo r2 = 0.380, AUC = 0.889). The results revealed indicate a potential cumulative gene effect, confirming the involvement of dopaminergic and GABAergic neurotransmission, the reelin pathway and the role of alpha-synuclein in musicality formation.

Research target: Psychology Biology
Language: English
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Keywords: музыкаматематическая модельmusicлогистическая регрессиякогнитивные способности mathematical modeldopaminepanel logistic regressionдофамин cognitive abilities
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