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The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.

Strategic thinking is a complex construct that refers to the cognitive activities of individual strategists. A literature review has identified five dimensions of strategic thinking, each exhibiting a dual nature: attitude towards the environment (confrontational vs. cooperative); level of participation (individual vs. collective); approach to change (exploitation vs. exploration); information processing style (intuitive vs. analytical); and level of flexibility (planned vs. emergent). This article aims to identify how these individual characteristics of strategic thinking cluster and whether recurring patterns can be observed, leading to a typology of strategic thinkers. The study adopts an exploratory approach, with quantitative research conducted on a sample of strategic thinkers in Poland (n=184) using an original validated scale. To identify relationships between the characteristics of strategic thinking, statistical methods for dependency analysis of categorical traits were applied, including multidimensional correspondence analysis and cluster analysis. The research identified three types of strategic thinkers, with correspondence analysis most clearly indicating a type embodied by a confrontational, intuitive, exploring, and planning individualist leader. The quality of the mappings obtained through multidimensional correspondence analysis demonstrates the utility of this method for profiling strategic thinking styles, while hierarchical cluster analysis effectively supports the interpretation of the correspondence analysis results.

We have been aware of the existence of knotted proteins for over 30 years—but it is hard to predict what is the most complicated knot that can be formed in proteins. Here, we show new and the most complex knotted topologies recorded to date—double trefoil knots (31#31). We found five domain arrangements (architectures) that result in a doubly knotted structure in almost a thousand proteins. The double knot topology is found in knotted membrane proteins from the CaCA family, that function as ion transporters, in the group of carbonic anhydrases that catalyze the hydration of carbon dioxide, and in the proteins from the SPOUT superfamily that gathers 31 knotted methyltransferases with the active site-forming knot. For each family, we predict the presence of a double knot using AlphaFold and RoseTTaFold structure prediction. In the case of the TrmD-Tm1570 protein, which is a member of SPOUT superfamily, we show that it folds in vitro and is biologically active. Our results show that this protein forms a homodimeric structure and retains the ability to modify tRNA, which is the function of the single-domain TrmD protein. However, how the protein folds and is degraded remains unknown.

Fish passes are essential elements for maintaining continuity for migrating fish. Without them, fish would be unable to undertake migration to satisfy their basic life needs. These devices must meet a range of requirements related to the size of individual fish pass elements and the hydrodynamic parameters of the flowing water. Despite efforts, it is not always possible to meet these requirements. There are many causes of errors in the design and construction of fish passes, and each case should be assessed individually. The most severe consequence of these errors is the obstruction of fish migrating upstream. In this study, an analysis of the permeability of a semi-natural fish pass was conducted for fish. This assessment was carried out using two methods. In the first approach, the required geometric dimensions of the fish pass elements were determined based on the dimensions of individuals living in the river channel. In the second approach, the dimensions were extracted from publications dedicated to slot fish passes, as the studied object resembles such a design. The analysis revealed that the fish pass does not fulfil its intended role. All fish species living in the Nidzica River channel face difficulties in passing through the fish pass, including the brook trout, for which the object is specifically designed. The main errors stem from the design and construction, resulting in exceeded values, primarily in the hydrodynamic parameters, rendering the fish pass impassable. The study also aimed to develop corrective recommendations considering the latest scientific developments.

Abstract In this work, we ask for and answer what makes classical temporal-difference reinforcement learning with $$\epsilon$$ ϵ -greedy strategies cooperative. Cooperating in social dilemma situations is vital for animals, humans, and machines. While evolutionary theory revealed a range of mechanisms promoting cooperation, the conditions under which agents learn to cooperate are contested. Here, we demonstrate which and how individual elements of the multi-agent learning setting lead to cooperation. We use the iterated Prisoner’s dilemma with one-period memory as a testbed. Each of the two learning agents learns a strategy that conditions the following action choices on both agents’ action choices of the last round. We find that next to a high caring for future rewards, a low exploration rate, and a small learning rate, it is primarily intrinsic stochastic fluctuations of the reinforcement learning process which double the final rate of cooperation to up to 80%. Thus, inherent noise is not a necessary evil of the iterative learning process. It is a critical asset for the learning of cooperation. However, we also point out the trade-off between a high likelihood of cooperative behavior and achieving this in a reasonable amount of time. Our findings are relevant for purposefully designing cooperative algorithms and regulating undesired collusive effects.

Abstract The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

Novel hybrid TiO2-based materials were obtained by adsorption of two different porphyrins on the surface of nanoparticles—commercially available 5,10,15,20-tetrakis(4-sulfonatophenyl)porphyrin (TPPS) and properly modified metalloporphyrin—5,10,15,20-tetrakis(2,6-difluoro-3-sulfophenyl)porphyrin palladium(II) (PdF2POH). The immobilization of porphyrins on the surface of TiO2 was possible due to the presence of sulfonyl groups. To further elevate the adsorption of porphyrin, an anchoring linker—4-hydroxybenzoic acid (PHBA)—was used. The synthesis of hybrid materials was proven by electronic absorption spectroscopy, dynamic light scattering (DLS), and photoelectrochemistry. Results prove the successful photosensitization of TiO2 to visible light by both porphyrins. However, the presence of the palladium ion in the modifier structure played a key role in strong adsorption, enhanced charge separation, and thus effective photosensitization. The incorporation of halogenated metalloporphyrins into TiO2 facilitates the enhancement of the comprehensive characteristics of the investigated materials and enables the evaluation of their performance under visible light. The effectiveness of reactive oxygen species (ROS) generation was also determined. Porphyrin-based materials with the addition of PHBA seemed to generate ROS more effectively than other composites. Interestingly, modifications influenced the generation of singlet oxygen for TPPS but not hydroxyl radical, in contrast to PdF2POH, where singlet oxygen generation was not influenced but hydroxyl radical generation was increased. Palladium (II) porphyrin-modified materials were characterized by higher photostability than TPPS-based nanostructures, as TPPS@PHBA-P25 materials showed the highest singlet oxygen generation and may be oxidized during light exposure. Photocatalytic activity tests with two model pollutants—methylene blue (MB) and the opioid drug tramadol (TRML)—confirmed the light dose-dependent degradation of those two compounds, especially PdF2POH@P25, which led to the virtually complete degradation of MB.

RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.org/.

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt-a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)-a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

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