Denis Horváth

We demonstrate that group size reshapes the effective interaction network in confined biological collectives. Using minimal groups of the shrimp Neocaridina davidi (N=2,3,4) as a representative nonequilibrium system, we reconstruct the asymmetric coupling matrix via an inverse asymmetric Ising model. This mapping is physically enabled by thigmotactic behavior, which restricts trajectories to a discrete decision space, allowing for a maximum-entropy description appropriate for steady states characterized by broken detailed balance. Our analysis reveals a structured progression with increasing group size: Mean coupling strength undergoes social screening, while heterogeneity, structural frustration, and nonreciprocal asymmetry emerge as defining architectural features. Group-size identity is encoded in a subspace orthogonal to the global coupling scale and statistical volatility. Furthermore, the eigenvalue spectrum signals a stationary log-probability distribution in which collective rotational order is amplified while statistical barriers between configurations are lowered, thereby expanding the repertoire of accessible states. Confinement thus drives a well-defined architectural transition from simple reciprocal coordination toward a complex, heterogeneous, and nonreciprocal nonequilibrium steady state.

In this study, we explore self-organization in a two-dimensional swarm model composed of two distinct swarmalator types (A and B) with opposing angular velocities, focusing on the roles of stigmergy-mediated interactions, phase-dependent motion, and physical constraints, including boundary conditions and barrier effects. Unlike traditional approaches that rely on local or direct physical interactions, the proposed swarm coordinates through internal phase-based dynamics coupled with environmental phase modifications. The intrinsic angular velocity of each swarmalator determines its phase rotation, which in turn governs its orbital motion and phase interactions with the environment. The nonequilibrium simulations reveal varying degrees of segregation across different segregation stages, observed across different time horizons, which are quantified by pairwise measures of neighborhood homogeneity specific to swarmalator type (A or B). Corotating pairs (AA or BB) channel trajectories via stigmergy, forming uniform clusters of the same type. In contrast, counter-rotating pairs (AB or BA) induce phase mixing, cluster decay, and fluctuations at cluster peripheries. At high stigmergy-mediated coupling, dynamic ring-shaped clusters emerge, with their macroscopic rotation direction determined by the sign of the angular velocity. Our findings underscore the importance of the interplay between indirect interactions and spatial constraints in shaping emergent self-organized patterns. This work provides a blueprint for constructing bioinspired robotic systems that adapt through environmental feedback, paving the way for advanced environments designed to actively guide swarm behaviors.

In quasi-one-dimensional circularly symmetric systems of active particles, experiments and simulations reveal an indirect interplay between particles and environmental drag effects, proving crucial in the realm of generalized parametrically controlled stigmergy. Our investigation goes deeper into understanding how stigmergy manifests itself, closely examining unconventional, more physically grounded interpretations in contrast to established concepts. Deeper insights into the complex dynamics of stigmergically interacting particle systems are gained by systematically studying the transition regions between short- and long-term stigmergic effects. Mechanical and computational modeling techniques complement each other to provide a comprehensive understanding of various clustering patterns, oscillatory modes, and system dynamics, where hysteresis may occur depending on the conditions.

β-actin belongs to cytoskeletal structures that change dynamically in cells according to various stimuli. Human skin can be considered as an organ that is very frequently exposed to various stress factors, of which light plays an important role. The present study focuses on adult human fibroblasts exposed to two types of light stress. Orange light with a wavelength of 590 nm was used here to stimulate the photosensitizer localized in the cells as a residual dose of photodynamic therapy (PDT). On the other hand, near-infrared light with a wavelength of 808 nm was considered for photobiomodulation (PBM), which is often used in healing processes. Confocal fluorescence microscopy was used to observe changes in intercellular communication, mitochondrial structures, and cytoskeletal dynamics defined by the remodulation of β-actin of fibroblasts. The number of β-actin bundles forming spherical structures was detected after light exposure. These structures as β-actin oligomers were confirmed with super-resolution microscopy. While PDT led to the disintegration of actin oligomers, PBM increased their number. The interaction of β-actin with mitochondria was observed. The combination of PDT and PBM treatments is important to minimize the side effects of cancer treatment with PDT on healthy cells, as shown by the cell metabolism assay in this work. In this work, β-actin is presented as an important parameter that changes and is involved in the response of cells to PDT and PBM.

This study aims to examine experimental conditions in which active particles are forced by their surroundings to move forward and backward in a continuous oscillatory manner. The experimental design is based on using a vibrating self-propelled toyrobot called hexbug, which is placed inside a narrow channel closed on one end by a rigid moving wall. Using the end-wall velocity as a controlling factor, the main forward mode of the hexbug movement can be turned to mostly rearward mode. We investigate the bouncing hexbug motion on both experimental and theoretical grounds. The Brownian model of active particles with inertia is employed in the theoretical framework. The model itself uses a pulsed Langevin equation in order to simulate abrupt changes in velocity that mimic hexbug propulsion in the moments when its legs make contact with the base plate. Significant directional asymmetry is caused by the legs bending backward. We demonstrate that the simulation successfully reproduces the experimental characteristics of hexbug motion after regressing the spatial and temporal statistical characteristics, especially when directional asymmetry is under consideration.

Deregulation of signalling pathways that regulate cell growth, survival, metabolism, and migration can frequently lead to the progression of cancer. Brain tumours are a large group of malignancies characterised by inter- and intratumoral heterogeneity, with glioblastoma (GBM) being the most aggressive and fatal. The present study aimed to characterise the expression of cancer pathway-related genes (n = 84) in glial tumour cell lines (A172, SW1088, and T98G). The transcriptomic data obtained by the qRT-PCR method were compared to different control groups, and the most appropriate control for subsequent interpretation of the obtained results was chosen. We analysed three widely used control groups (non-glioma cells) in glioblastoma research: Human Dermal Fibroblasts (HDFa), Normal Human Astrocytes (NHA), and commercially available mRNAs extracted from healthy human brain tissues (hRNA). The gene expression profiles of individual glioblastoma cell lines may vary due to the selection of a different control group to correlate with. Moreover, we present the original multicriterial decision making (MCDM) for the possible characterization of gene expression profiles. We observed deregulation of 75 genes out of 78 tested in the A172 cell line, while T98G and SW1088 cells exhibited changes in 72 genes. By comparing the delta cycle threshold value of the tumour groups to the mean value of the three controls, only changes in the expression of 26 genes belonging to the following pathways were identified: angiogenesis FGF2; apoptosis APAF1, CFLAR, XIAP; cellular senescence BM1, ETS2, IGFBP5, IGFBP7, SOD1, TBX2; DNA damage and repair ERCC5, PPP1R15A; epithelial to mesenchymal transition SNAI3, SOX10; hypoxia ADM, ARNT, LDHA; metabolism ATP5A1, COX5A, CPT2, PFKL, UQCRFS1; telomeres and telomerase PINX1, TINF2, TNKS, and TNKS2. We identified a human astrocyte cell line and normal human brain tissue as the appropriate control group for an in vitro model, despite the small sample size. A different method of assessing gene expression levels produced the same disparities, highlighting the need for caution when interpreting the accuracy of tumorigenesis markers.

Photopolymer nanowires prepared by two-photon polymerization direct laser writing (TPP-DLW) are the building blocks of many microstructure systems. These nanowires possess viscoelastic characteristics that define their deformations under applied forces when operated in a dynamic regime. A simple mechanical model was previously used to describe the bending recovery motion of deflected nanowire cantilevers in Newtonian liquids. The inverse problem is targeted in this work; the experimental observations are used to determine the nanowire physical characteristics. Most importantly, based on the linear three-parameter solid model, we derive explicit formulas to calculate the viscoelastic material parameters. It is shown that the effective elastic modulus of the studied nanowires is two orders of magnitude lower than measured for the bulk material. Additionally, we report on a notable effect of the surrounding aqueous glucose solution on the elasticity and the intrinsic viscosity of the studied nanowires made of Ormocomp.

Self-organized wireless networks based on unmanned aerial vehicles (UAVs) are receiving increasing attention due to their flexible deployment opportunities. Typically, UAVs are used as supplementary access points to existing ground infrastructure to extend network coverage and capacity. Therefore, finding the optimal position for each UAV is a crucial task that can be resolved by multiobjective optimization. In this paper, a self-organizing UAV swarm model is introduced as an evolutionary type of heuristic that can be adjusted in real time based on the network’s key performance indicators (KPIs). The exogenous dynamics of the wireless communication system are reflected by an end-user mobility model based on Ornstein-Uhlenbeck processes. To achieve versatility in the positioning of UAVs, a model of virtual forces is adopted to enable control of the movement of each UAV via a collection of persistent and mutable parameters of the system. Evolution is controlled by eleven strategic versions of parametric mutations, including a neutral benchmark process. The simulation results reveal that the suggested solution allows for a substantial increase in the KPI efficiency metrics relative to those of the nonmutated system. The results also addressed the presence of the Pareto front, where it is not possible to improve the selected KPIs without negatively affecting others. While existing research that focuses on evolutionary methods usually considers offline approaches, this work uses an online evolutionary approach that is able to solve the presented problem by taking the dynamic nature of the environment into account.