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Recent advancements in genomics, propelled by artificial intelligence, have unlocked unprecedented capabilities in interpreting genomic sequences, mitigating the need for exhaustive experimental analysis of complex, intertwined molecular processes inherent in DNA function. A significant challenge, however, resides in accurately decoding genomic sequences, which inherently involves comprehending rich contextual information dispersed across thousands of nucleotides. To address this need, we introduce GENA language model (GENA-LM), a suite of transformer-based foundational DNA language models capable of handling input lengths up to 36 000 base pairs. Notably, integrating the newly developed recurrent memory mechanism allows these models to process even larger DNA segments. We provide pre-trained versions of GENA-LM, including multispecies and taxon-specific models, demonstrating their capability for fine-tuning and addressing a spectrum of complex biological tasks with modest computational demands. While language models have already achieved significant breakthroughs in protein biology, GENA-LM showcases a similarly promising potential for reshaping the landscape of genomics and multi-omics data analysis. All models are publicly available on GitHub (https://github.com/AIRI-Institute/GENA_LM) and on HuggingFace (https://huggingface.co/AIRI-Institute). In addition, we provide a web service (https://dnalm.airi.net/) allowing user-friendly DNA annotation with GENA-LM models., (© The Author(s) 2025. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Associative learning is a behavioral phenomenon in which individuals develop connections between stimuli or events based on their co-occurrence. Initially studied by Pavlov in his conditioning experiments, the fundamental principles of learning have been expanded on through the discovery of a wide range of learning phenomena. Computational models have been developed based on the concept of minimizing reward prediction errors. The Rescorla-Wagner model, in particular, is a well-known model that has greatly influenced the field of reinforcement learning. However, the simplicity of these models restricts their ability to fully explain the diverse range of behavioral phenomena associated with learning. In this study, we adopt the free energy principle, which suggests that living systems strive to minimize surprise or uncertainty under their internal models of the world. We consider the learning process as the minimization of free energy and investigate its relationship with the Rescorla-Wagner model, focusing on the informational aspects of learning, different types of surprise, and prediction errors based on beliefs and values. Furthermore, we explore how well-known behavioral phenomena such as blocking, overshadowing, and latent inhibition can be modeled within the active inference framework. We accomplish this by using the informational and novelty aspects of attention, which share similar ideas proposed by seemingly contradictory models such as Mackintosh and Pearce-Hall models. Thus, we demonstrate that the free energy principle, as a theoretical framework derived from first principles, can integrate the ideas and models of associative learning proposed based on empirical experiments and serve as a framework for a better understanding of the computational processes behind associative learning in the brain., (© 2024 Massachusetts Institute of Technology.)

Developmental plasticity enables organisms to cope with new environmental challenges. If deploying such plasticity is costly in terms of time or energy, the same adaptive behaviour could subsequently evolve through piecemeal genomic reorganisation that replaces the requirement to acquire that adaptation by individual plasticity. Here, we report a new dimension to the way in which plasticity can drive evolutionary change, leading to an ever-greater complexity in biological organisation. Plasticity dramatically accelerates the evolutionary accumulation of adaptive systems in model organisms with relatively low rates of mutation. The effect of plasticity on the evolutionary growth of complexity is even greater when the number of elements needed to construct a functional system is increased. These results suggest that, as the difficulty of challenges from the environment becomes greater, plasticity exerts an ever more powerful role in meeting those challenges and in opening up new avenues for the subsequent evolution of complex adaptations.

Corrective lower limb osteotomies are innovative and efficient therapeutic procedures for restoring axial alignment and managing unicompartmental knee osteoarthritis. This review presents critical insights into the up-dated clinical knowledge on osteotomies for complex posttraumatic or congenital lower limb deformities with a focus on high tibial osteotomies, including a comprehensive overview of basic principles of osteotomy planning, biomechanical considerations of different implants for osteotomies and insights in specific bone deformity correction techniques. Emphasis is placed on complex cases of lower limb osteotomies associated with ligament and multiaxial instability including pediatric cases, computer-assisted navigation, external fixation for long bone deformity correction and return to sport after such osteotomies. Altogether, these advances in the experimental and clinical knowledge of complex lower limb osteotomies allow generating improved, adapted therapeutic regimens to treat congenital and acquired lower limb deformities.

The plasticity of neural networks is a complex process determined by changes in physiological status, gene expression and phenotype of a cell. A detailed study of this process dynamics requires the simultaneous recording of electrical and genomic activities in networks of neurons. This sets up one of the tasks for modern neuroscience as development of integration of electrophysiology and molecular biology methods. In the paper we review the current approaches to such integration, as well as the choice of molecular markers for detection of genomic and synaptic plasticity of neurons by use of physiological micro-sensorial system based on neuronal cells cultured on the micro-electrode arrays.

One of the greatest challenges in the modern biological and social sciences is to understand the evolution of cooperative behaviour. General outlines of the answer to this puzzle are currently emerging as a result of developments in the theories of kin selection, reciprocity, multilevel selection and cultural group selection. The main conceptual tool used in probing the logical coherence of proposed explanations has been game theory, including both analytical models and agent-based simulations. The game-theoretic approach yields clear-cut results but assumes, as a rule, a simple structure of payoffs and a small set of possible strategies. Here we propose a more stringent test of the theory by developing a computer model with a considerably extended spectrum of possible strategies. In our model, agents are endowed with a limited set of receptors, a set of elementary actions and a neural net in between. Behavioural strategies are not predetermined; instead, the process of evolution constructs and reconstructs them from elementary actions. Two new strategies of cooperative attack and defence emerge in simulations, as well as the well-known dove, hawk and bourgeois strategies. Our results indicate that cooperative strategies can evolve even under such minimalist assumptions, provided that agents are capable of perceiving heritable external markers of other agents.

Cavinton was used for 10 years in 967 patients with different cerebrovascular diseases. The highest effect was seen in patients with early forms and primarily chronic forms: vegetovascular (neurocirculatory) dystonia, initial manifestations of brain blood supply insufficiency, circulatory encephalopathy in the first and second stages. Improvement of the subjective status and a decrease of the intensity of vestibulocerebellar disorders were recorded by the end of the treatment in 75-85% of such patients. In ischemic brain stroke, regress of general cerebral and focal symptoms was more rapid and significant in the adequate reaction type of cerebral hemodynamics to cavinton administration (a rise of pulse blood content of the brain and a reduction of the vascular tone according to the REG data) and was less noticeable in the hypertonic and, in particular, in the hypotonic type. Cavinton should not be used in severe general cerebral hypertensive crises, as well as in elderly or senile patients with acute cardio-cerebral or cerebro-cardiac syndrome, postinfarction cardiosclerosis, marked disorders of heart rhythm.