Albumin is one of the major components of synovial fluid. Due to its negative surface charge, it plays an essential role in many physiological processes, including the ability to form molecular complexes. In addition, glycosaminoglycans such as hyaluronic acid and chondroitin sulfate are crucial components of synovial fluid involved in the boundary lubrication regime. This study presents the influence of Na${}^{+}$, Mg${}^{2+}$ and Ca${}^{2+}$ ions on human serum albumin–hyaluronan/chondroitin-6-sulfate interactions examined using molecular docking followed by molecular dynamics simulations. We analyze chosen glycosaminoglycans binding by employing a conformational entropy approach. In addition, several protein–polymer complexes have been studied to check how the binding site and presence of ions influence affinity. The presence of divalent cations contributes to the decrease of conformational entropy near carboxyl and sulfate groups. This observation can indicate the higher affinity between glycosaminoglycans and albumin. Moreover, domains IIIA and IIIB of albumin have the highest affinity as those are two domains that show a positive net charge that allows for binding with negatively charged glycosaminoglycans. Finally, in discussion, we suggest some research path to find particular features that would carry information about the dynamics of the particular type of polymers or ions.

The goal of the research is to describe the aggregation process inside the mucilage produced by plant seeds using molecular dynamics (MD) combined with time series algorithmic analysis based on the recurrence plots. The studied biological molecules model is seed mucilage composed of three main polysaccharides, i.e. pectins, hemicellulose, and cellulose. The modeling of biological molecules is based on the assumption that a classical–quantum passage underlies the aggregation process in the mucilage, resulting from non-covalent interactions, as they affect the macroscopic properties of the system. The applied recurrence plot approach is an important tool for time series analysis and data mining dedicated to analyzing time series data originating from complex, chaotic systems. In the current research, we demonstrated that advanced algorithmic analysis of seed mucilage data can reveal some features of the dynamics of the system, namely temperature-dependent regions with different dynamics of increments of a number of hydrogen bonds and regions of stable oscillation of increments of a number of hydrophobic–polar interactions. Henceforth, we pave the path for automatic data-mining methods for the analysis of biological molecules with the intermediate step of the application of recurrence plot analysis, as the generalization of recurrence plot applications to other (biological molecules) datasets is straightforward.

Recent decades have brought increasing effects of climate change and variation. Climate changes, indicated by various bioclimatic indices, influence the functioning of living organisms, including humans. Previous studies have documented multiyear fluctuations in such indices. In this study, we employ the Universal Thermal Climate Index (UTCI), a recently developed bioclimatic indicator designed to condense complex thermodynamic and meteorological conditions into a single value representing specific categories of thermal stress. The average annual UTCI values for the period 1826–2021 in Kraków, Poland, increased at a rate of $0.27~^{\circ }$ C per decade, with the most pronounced rise observed during the winter season, as documented in prior studies. However, when considering the more recent period of 1951–2023, the trend intensifies significantly, reaching $0.61~^{\circ }$ C per decade, indicating a clear acceleration in bioclimatic warming. In the present study, we apply an automatic recurrence plot (RP) analysis method to investigate these changes in human bioclimate. We introduce a novel approach by automating RP interpretation with a genetic algorithm and statistical deterministic analysis, thereby providing readers with ready-to-use algorithms. The research focuses on a univariate time series of UTCI recorded from 1951 to 2023 in three locations in Poland–a key region of Central Europe where air masses from different parts of the continent interact. The main conclusion from the analyzed data is the identification of a date range (1980–1990), making a phase-transition-type passage between two periods that differ in statistical behaviors of UTCI. This bioclimatic change led to an increase in the frequency of very strong heat stress events and a decrease in the occurrence of very strong cold stress days. The RP analysis demonstrates that the data from 1990 differ most from data from the preceding period 1952–1965. This provides details of the findings reported in previous research regarding climate change in Poland. This discovery of a distinct transitional period marking a shift in the climatic regime in Poland, based on RP analysis, enriches existing knowledge on regional climate change with new empirical evidence.

We present the statistical method to study the interaction between a chosen protein and another molecule (e.g., both being components of lubricin found in synovial fluid) in a water environment. The research is performed on the example of univariate time series of chosen features of the dynamics of mucin, which interact with chondroitin sulfate (4 and 6) in four different saline solutions. Our statistical approach is based on recurrence methods to analyze chosen features of molecular dynamics. Such recurrence methods are usually applied to reconstruct the evolution of a molecular system in its reduced phase space, where the most important variables in the process are taken into account. In detail, the analyzed time-series are spitted onto sub-series of records that are expected to carry meaningful information about the system of molecules. Elements of sub-series are splinted by the constant delay-time lag (that is the parameter determined by statistical testing in our case), and the length of sub-series is the embedded dimension parameter (using the Cao method). We use the recurrent plots approach combined with the Shannon entropy approach to analyze the robustness of the sub-series determination. We hypothesize that the robustness of the sub-series determines some specifics of the dynamics of the system of molecules. We analyze rather highly noised features to demonstrate that such features lead to recurrence plots that graphically look similar. From the recurrence plots, the Shannon entropy has been computed. We have, however, demonstrated that the Shannon entropy value is highly dependent on the delay time value for analyzed features. Hence, elaboration of a more precise method of the recurrence plot analysis is required. For this reason, we suggest the random walk method that can be applied to analyze the recurrence plots automatically.