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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Phonetic clustering characteristics in verbal fluency: A potential marker for differentiating subjective cognitive decline from mild cognitive impairment

Applied Neuropsychology: Adult. 2026. P. 1–11.
Cherkasov N., Rodionova E., Arina Zvereva, Kolykhalov I., Malyutina S.

Objective

Semantic and phonemic verbal fluency (VF) tasks are widely used to assess older adults’ cognition in clinical practice. Typical scoring only analyses the total number of correct words produced. We investigated whether differentiation between individuals with subjective cognitive decline (SCD) versus mild cognitive impairment (MCI), which is often challenging, could be enhanced by also assessing linguistic (clustering) characteristics of VF responses.

Method

In this cross-sectional study, we retrospectively analyzed 426 VF responses from 127 community-dwelling older adults with SCD or MCI who underwent cognitive assessment in a memory clinic setting. Using mixed-effect models, we tested whether including linguistic (clustering) characteristics in addition to the total number of correct words enhanced the prediction of the Montreal Cognitive Assessment (MoCA) score and clinical group (SCD or MCI), when adjusted for age, sex, education level, and repeated measures alongside with task type.

Results

A lower mean phonetic cluster size in both SVF and PVF was associated with a lower MoCA score. Adding clustering characteristics to the model of SVF (although not PVF) significantly improved classification into SCD versus MCI, compared to the model with the number of correct words alone.

Conclusions

Retrieving sequences of words based on their phonetic proximity while performing not only PVF but also SVF tasks pointed to more preserved cognitive functioning and appeared vulnerable to early cognitive changes. Linguistic analysis of VF performance can capture subtle cognitive reorganization missed by scoring of the total number of correct words only and may enhance early dementia-risk profiling.

Research target: Clinical Medicine Philology and Linguistics
Language: English
Full text
DOI
Text on another site
Keywords: elderlycross-sectional studyverbal fluencymild cognitive impairment Agingphonetic clusterssemantic clusterssubjective cognitive decline
Publication based on the results of:
Neural foundations of language and its impairments: From basic research to clinical practice (2025)
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