Eva Koroleva

RNA structure prediction is a fundamental challenge in molecular biology with significant impli- cations for medicine, biotechnology, and our understanding of life. In this paper, we present an enhanced transformer-based architecture for predicting RNA structure and chemical reactivity pro- files. Our approach integrates base pair probability matrices (BPPMs) with sequence information through a novel convolutional-attention mechanism and incorporates dynamic positional bias to better generalize to sequences of varying lengths. We introduce a Squeeze-and-Excitation enhancement to convolutional blocks that improves feature extraction from BPPMs and develop a specialized model for cross-reactivity prediction. Our ensemble approach achieves a mean absolute error (MAE) of 0.0626 on the RNA reactivity dataset, representing a significant improvement over existing methods. We analyze the contribution of various architectural components and demonstrate that our approach effectively captures the complex interactions between nucleotides that determine RNA structure. The proposed model has potential applications in RNA-based drug design, understanding genetic diseases, and developing novel therapeutics.

Automation in the analysis of biological data can enhance the accuracy of results and reduce processing time. Analyzing microscopic images is a common task in biology. To demonstrate the proposed method, we analyzed collagen structures in dermal tissue images. A methodology for automatic analysis of microscope images is presented. The object of analysis can be identified using a color vector. The image is then binarized and meshed. For each mesh element, the distribution of mean intercept length (MIL) is calculated. The orientation of objects is estimated using MIL approximation. An equation is proposed to assess the quality of collagen recovery. This method was applied to three sample groups: no ficin (N), ficin-treated (F), and immobilized ficin (Fi). We analyzed 10 images per group and obtained results using the described technique. Collagen recovery quality was: N group – 48% ± 8%, F group – 78% ± 7%, and Fi group – 68% ± 9%. These results suggest that ficin positively influences dermal healing. The obtained data are consistent with previously published studies.

This study presents a methodology for constructing a finite element model (FEM) based on computed tomography (CT) data. The approach was applied to model the femur, emphasizing the importance of capturing the spatial distribution of mechanical properties in bone tissue and the need for individualized modeling. Finite element simulations were performed using ANSYS, and CT data were processed in Avizo. The analysis considered a linear inhomogeneous elastic material. Power-law relationships between optical density and mechanical properties—specifically Young’s modulus and ultimate stress—were employed. Optical density was derived from linear correlations with Hounsfield units. Mechanical properties were assigned to each finite element based on CT data. After solving the stress-strain problem, a safety factor was calculated for each node, taking into account the local material properties. Both homogeneous and inhomogeneous models were developed and analyzed. The results clearly demonstrate significant differences in stress-strain responses between the two models. The inhomogeneous model enables evaluation of local bone strength, accounting for patient-specific characteristics.

This study addresses the problem of determining the macroscopic mechanical properties of porous materials based on their internal structure. The structure was characterized using the fabric tensor and porosity. An experimental investigation was carried out using a two-component cold-curing liquid polyurethane plastic (Lasilcast Lc-12). The fabricated samples were scanned using computed tomography, and the resulting data were analyzed to extract structural characteristics. A representative subvolume was selected for further analysis. Mechanical testing was then conducted to determine the fabric tensor, porosity, Young’s modulus, and Poisson’s ratio of the samples. Results are presented for several specimens. Furthermore, we examined how macroscopic mechanical properties depend on the nature of the porous structure, particularly considering porosity variation. To evaluate this dependence, we established relationships between Young’s modulus and Poisson’s ratio and two structural parameters: pore orientation angle (α) and pore ellipticity (λ). The sensitivity of deformation behavior to elastic constants was also assessed.