Multivalent interactions drive the Toxoplasma AC9:AC10:ERK7 complex to concentrate ERK7 in the apical cap

The Toxoplasma inner membrane complex (IMC) is a specialized organelle that is crucial for the parasite to establish an intracellular lifestyle and ultimately cause disease. The IMC is composed of both membrane and cytoskeletal components, further delineated into the apical cap, body, and basal subcompartments. The apical cap cytoskeleton was recently demonstrated to govern the stability of the apical complex, which controls parasite motility, invasion, and egress. While this role was determined by individually assessing the apical cap proteins AC9, AC10, and the MAP kinase ERK7, how the three proteins collaborate to stabilize the apical complex is unknown. In this study, we use a combination of deletion analyses and yeast-2-hybrid experiments to establish that these proteins form an essential complex in the apical cap. We show that AC10 is a foundational component of the AC10:AC9:ERK7 complex and demonstrate that the interactions among them are critical to maintain the apical complex. Importantly, we identify multiple independent regions of pairwise interaction between each of the three proteins, suggesting that the AC9:AC10:ERK7 complex is organized by multivalent interactions. Together, these data support a model in which multiple interacting domains enable the oligomerization of the AC9:AC10:ERK7 complex and its assembly into the cytoskeletal IMC, which serves as a structural scaffold that concentrates ERK7 kinase activity in the apical cap.

Intelligent Control of Swarm Robotics Employing Biomimetic Deep Learning

The collective motion of biological species has robust and flexible characteristics. Since the individual of the biological group interacts with other neighbors asymmetrically, which means the pairwise interaction presents asymmetrical characteristics during the collective motion, building the model of the pairwise interaction of the individual is still full of challenges. Based on deep learning (DL) technology, experimental data of the collective motion on Hemigrammus rhodostomus fish are analyzed to build an individual interaction model with multi-parameter input. First, a Deep Neural Network (DNN) structure for pairwise interaction is designed. Then, the interaction model is obtained by means of DNN proper training. We propose a novel key neighbor selection strategy, which is called the Largest Visual Pressure Selection (LVPS) method, to deal with multi-neighbor interaction. Based on the information of the key neighbor identified by LVPS, the individual uses the properly trained DNN model for the pairwise interaction. Compared with other key neighbor selection strategies, the statistical properties of the collective motion simulated by our proposed DNN model are more consistent with those of fish experiments. The simulation shows that our proposed method can extend to large-scale group collective motion for aggregation control. Thereby, the individual can take advantage of quite limited local information to collaboratively achieve large-scale collective motion. Finally, we demonstrate swarm robotics collective motion in an experimental platform. The proposed control method is simple to use, applicable for different scales, and fast for calculation. Thus, it has broad application prospects in the fields of multi-robotics control, intelligent transportation systems, saturated cluster attacks, and multi-agent logistics, among other fields.

Desolvation Energy Explains Partitioning of Client Proteins into Condensates

Membraneless organelles are cellular compartments that form by liquid-liquid phase separation of one or more components. Other molecules, such as other proteins and nucleic acids, will distribute between the cytoplasm and the liquid compartment in accordance with the thermodynamic drive to lower the free energy of the system. The resulting distribution colocalizes molecular species, to carry out a diversity of functions. Two factors could drive this partitioning: the difference in solvation between the dilute versus dense phase, and intermolecular interactions between the client and scaffold proteins. Here, we develop a set of knowledge-based potentials that allow for the direct comparison between desolvation energy and pairwise interaction energy terms, and use these to examine experimental data from two systems: protein cargo dissolving within phase-separated droplets made from FG repeat proteins of the nuclear pore complex, and client proteins dissolving within phase-separated FUS droplets. We find close agreement between desolvation energies of the client proteins and the experimentally determined values of the partition coefficients, while pairwise interaction energies between client and scaffold show weaker correlations. These results show that client stickiness is sufficient to explain differential partitioning of clients within these two phase-separated systems without taking into account the composition of the condensate. This suggests that selective trafficking of client proteins to distinct membraneless organelles requires recognition elements beyond the client sequence composition.

Structural investigations into a new polymorph of F4TCNQ: towards enhanced semiconductor properties

During the course of research into the structure of 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ), C12F4N4, an important compound in charge-transfer and organic semiconductor research, a previously unreported polymorph of F4TCNQ was grown concomitantly with the known polymorph from a saturated solution of dichloromethane. The structure was elucidated using single-crystal X-ray diffraction and it was found that the new polymorph packs with molecules in parallel layers, in a similar manner to the layered structure of F2TCNQ. The structure was analysed using Hirshfeld surface analysis, fingerprint plots and pairwise interaction energies, and compared to existing data. The structure of a toluene solvate of F4TCNQ is also reported.

Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials

The rigid constraints of chemistry—dictated by quantum mechanics and the discrete nature of the atom—limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.

Branching processes with interactions: subcritical cooperative regime

In this paper, we introduce a family of processes with values on the nonnegative integers that describes the dynamics of populations where individuals are allowed to have different types of interactions. The types of interactions that we consider include pairwise interactions, such as competition, annihilation, and cooperation; and interactions among several individuals that can be viewed as catastrophes. We call such families of processes branching processes with interactions. Our aim is to study their long-term behaviour under a specific regime of the pairwise interaction parameters that we introduce as the subcritical cooperative regime. Under such a regime, we prove that a process in this class comes down from infinity and has a moment dual which turns out to be a jump-diffusion that can be thought as the evolution of the frequency of a trait or phenotype, and whose parameters have a classical interpretation in terms of population genetics. The moment dual is an important tool for characterizing the stationary distribution of branching processes with interactions whenever such a distribution exists; it is also an interesting object in its own right.

The features of solutions of dynamical systems with an external exciting load and non-conservative dynamical systems with pairwise interaction of degrees of freedom

When mathematical models are developed, as a rule, a number of assumptions is done, which makes it possible to simplify the model, reduce its dimension and simulation time, or use the dimension reduction method. When modeling non-conservative systems with pairwise interaction of degrees of freedom, e.g. mechatronic systems, an elastic aircraft in a flow, an aeroelastic aircraft with an automated control system, etc., there is a desire to reduce the problem to a conservative dynamic system with harmonic action. The study shows that despite the apparent similarity of the tasks, they have significant differences that cannot be ignored. Differences in the behavior of conservative dynamical systems and non-conservative dynamical systems with pair interaction of degrees of freedom are considered. The results are demonstrated on the simplest example with an analytical solution, and in the finite element software package MSC.Nastran. The results of the solution in MSC.Nastran are compared with the results of the analytical solution.

Predicting Attributes of Nodes Using Network Structure

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute a , using all attributes of its h -hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute a . We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.

The Combination of Pairwise and Group Interactions Promotes Consensus in Opinion Dynamics

In complex systems, agents often interact with others in two distinct types of interactions, pairwise interaction and group interaction. The Deffuant–Weisbuch model adopting pairwise interaction and the Hegselmann–Krause model adopting group interaction are the two most widely studied opinion dynamics. In this study, we propose a novel opinion dynamics by combining pairwise and group interactions for agents and study the effects of the combination on consensus in the population. In the model, we introduce a parameter α to control the weights of the two interactions in the dynamics. Through numerical simulations, we find that there exists an optimal α , which can lead to a highest probability of complete consensus and minimum critical bounded confidence for the formation of consensus. Furthermore, we show the effects of α on opinion formation by presenting the observations for opinion clusters. Moreover, we check the robustness of the results on different network structures and find the promotion of opinion consensus by α not limited to a complete graph.