Pairwise Interactions Research Articles

Evolution of cooperation in social dilemmas with assortment in finite populations

We investigate conditions for the evolution of cooperation in social dilemmas in finite populations with assortment of players by group founders and general payoff functions for cooperation and defection within groups. Using a diffusion approximation in the limit of a large population size that does not depend on the precise updating rule, we show that the first-order effect of selection on the fixation probability of cooperation when represented once can be expressed as the difference between time-averaged payoffs with respect to effective time that cooperators and defectors spend in direct competition in the different group states. Comparing this fixation probability to its value under neutrality and to the corresponding fixation probability for defection, we deduce conditions for the evolution of cooperation. We show that these conditions are generally less stringent as the level of assortment increases under a wide range of assumptions on the payoffs such as additive, synergetic or discounted benefits for cooperation, fixed cost for cooperation and threshold benefit functions. This is not necessarily the case, however, when payoffs in pairwise interactions are multiplicatively compounded within groups.

Genetic-epigenetic aspects of infertility in women with long-COVID-19

The aim of the study is to determine the genetic and epigenetic features of infertile patients with long-term COVID-19 in order to clarify the risk factors for the failure of assisted reproductive technologies (ART) use. Materials and methods. The MTHFR (C677T, A1298С), RFC-1 (G80A) and BHMT (G742A) gene polymorphisms and the methylation of ESR1 gene was performed. 40 patients (main group) with infertility due to long-term COVID-19 were examined: the subgroup 1 - 25 women in whom the use of ART was ineffective, the subgroup 2 - 15 patients with effective use of ART. The control was taken from literary sources: for RFC (G80A), MTHFR (C677T) and MTHFR (A1298C) polymorphisms - 35 Ukrainian women without infertility and reproductive losses; for BHMT (G742A) - 60 people of the Ukrainian population. The methods of variational statistics were used, in particular the Fisher test with a significance level of p<0.05. Results. Patients of the main group have a higher frequency of the homozygous genotype of the MTHFR gene polymorphism (C677T) on the mutant T allele (20.0% vs. 3.2%; p<0.05). When ART is unsuccessful, the TT genotype is 4 times greater (28.0% vs. 6.7%; p<0.05). A study by genotypes of MTHFR polymorphism (A1298C) did not reveal a significant difference. Patients of the main group `have a higher frequency of the mutant allele A of the RFC gene (G80A) (80.0% vs. 51.4%; p<0.05). No significant difference was found depending on the success rate of ART. The frequency of BHMT gene polymorphism (G742A) in the main group did not differ from that in the Ukrainian population, however, in the case of unsuccessful ART, it was observed less often and only in the heterozygous variant (40.0% vs. 66.6%; p<0.05). The analysis of pairwise intergenic interaction revealed the highest frequency of the combination of AAGA for the MTHFR (A1298C)+BHMT (G742A) pair - 35.0% and GAAA for the RFC (G80A)+MTHFR (A1298C) pair - 30.0% in the main group. Hypermethylation of the promoter region of the ESR1 gene is observed in 20 (50.0%) patients of the main group: in 17 (68.0%) women of the subgroup 1 versus 3 (20.0%) women of the subgroup 2 (p<0.05). Conclusions. The genetic and epigenetic conditioning of the success of ART programs in infertility associated with long-term COVID-19 has been revealed, which opens up new diagnostic and therapeutic opportunities for identifying factors predisposing to unsuccessful ART treatment and increasing the effectiveness of such treatment by correcting disorders of folic acid metabolism, hyperhomocysteinemia and the estrogens receptor apparatus. The research was carried out in accordance with the principles of the Declaration of Helsinki. The research protocol was approved by the Local Ethics Committee of the institution mentioned in the work. Informed consent of the women was obtained for the research. The authors declare no conflict of interest.

Selective decision-making and collective behavior of fish by the motion of visual attention.

Collective motion provides a spectacular example of self-organization in Nature. Visual information plays a crucial role among various types of information in determining interactions. Recently, experiments have revealed that organisms such as fish and insects selectively utilize a portion, rather than the entirety, of visual information. Here, focusing on fish, we propose an agent-based model where the direction of attention is guided by visual stimuli received from the images of nearby fish. Our model reproduces a branching phenomenon where a fish selectively follows a specific individual as the distance between two or three nearby fish increases. Furthermore, our model replicates various patterns of collective motion in a group of agents, such as vortex, polarized school, swarm, and turning. We also discuss the topological nature of the visual interaction, as well as the positional distribution of nearby fish and the map of pairwise and three-body interactions induced by them. Through a comprehensive comparison with existing experimental results, we clarify the roles of visual interactions and issues to be resolved by other forms of interactions.

Periplasmic electron transfer network in Geobacter sulfurreducens revealed by biomolecular interaction studies

AbstractMultiheme cytochromes located in different compartments are crucial for extracellular electron transfer in the bacterium Geobacter sulfurreducens to drive important environmental processes and biotechnological applications. Recent studies have unveiled that for particular sets of electron terminal acceptors, discrete respiratory pathways selectively recruit specific cytochromes from both the inner and outer membranes. However, such specificity was not observed for the abundant periplasmic cytochromes, namely the triheme cytochrome family PpcA‐E. In this work, the distinctive NMR spectroscopic signatures of these proteins in different redox states were explored to monitor pairwise interactions and electron transfer reactions between each pair of cytochromes. The results showed that the five proteins interact transiently and can exchange electrons between each other revealing intra‐promiscuity within the members of this family. This discovery is discussed in the light of the establishment of an effective electron transfer network by this pool of cytochromes. This network is advantageous to the bacteria as it enables the maintenance of the functional working potential redox range within the cells.

Exploring the influence of metal cations on individual hydrogen bonds in Watson-Crick guanine-cytosine DNA base pair: An interacting quantum atoms analysis.

This study delves into the nature of individual hydrogen bonds and the relationship between metal cations and hydrogen bonding in the Watson-Crick guanine-cytosine (GC) base pair and its alkali and alkaline earth cation-containing complexes (Mn+-GC). The findings reveal how metal cations affect the nature and strength of individual hydrogen bonds. The study employs interacting quantum atoms (IQA) analysis to comprehensively understand three individual hydrogen bonds within the GC base pair and its cationic derivatives. These analyses unveil the nature and strength of hydrogen bonds and serve as a valuable reference for exploring the impact of cations (and other factors) on each hydrogen bond. All the H D interactions (H is hydrogen and D is oxygen or nitrogen) in the GC base pair are primarily electrostatic in nature, with the charge transfer component playing a substantial role. Introducing a metal cation perturbs all H D interatomic interactions in the system, weakening the nearest hydrogen bond to the cation (indicated by a) and reinforcing the other (b and c) interactions. Notably, the interaction a, the strongest H D interaction in the GC base pair, becomes the weakest in the Mn+-GC complexes. A broader perspective on the stability of GC and Mn+-GC complexes is provided through interacting quantum fragments (IQF) analysis. This approach considers all pairwise interactions between fragments and intra-fragment components, offering a complete view of the factors that stabilize and destabilize GC and Mn+-GC complexes. The IQF analysis underscores the importance of electron sharing, with the dominant contribution arising from the inter-fragment exchange-correlation term, in shaping and sustaining GC and Mn+-GC complexes. From this point of view, alkaline and alkaline earth cations have distinct effects, with alkaline cations generally weakening inter-fragment interactions and alkaline earth cations strengthening them. In addition, IQA and IQF calculations demonstrate that the hydration of cations led to small changes in the hydrogen bonding network. Finally, the IQA interatomic energies associated with the hydrogen bonds and also inter-fragment interaction energies provide robust indicators for characterizing hydrogen bonds and complex stability, showing a strong correlation with total interaction energies.

Unraveling the Metabolic Behavior and Interspecific Interaction Pattern of Lactic Acid Bacteria within Chinese Rice Wine.

The synthetic community of lactic acid bacteria (LAB) is commonly utilized in the food industry for manipulating product properties. However, the intermediate interactions and ecological stability resulting from metabolic differences among various LAB types remain poorly understood. We aimed to analyze the metabolic behavior of single and combined lactic acid bacteria in China rice wine based on microbial succession. Three-stage succession patterns with obligate heterofermentative LAB dominating prefermentation and homofermentative LAB prevailing in main fermentation were observed. Facultative heterofermentative LAB exhibited significant growth. Pairwise coculture interactions revealed 63.5% positive, 34.4% negative, and 2.1% neutral interactions, forming nontransitive and transitive competition modes. Nontransitive competitive combinations demonstrated stability over ∼200 generations through amino acid (mainly aspartic acid, glutamine, and serine) cross-feeding and lactic acid detoxification, which also showed potential for controlling biogenic amines and developing LAB starter cultures. Our findings offer insights into the mechanistic underpinnings of LAB interaction networks.

In-Depth Understanding of the Effect of the Distribution of Substituents on the Morphology and Physical Properties of Ethylcellulose: Molecular Dynamics Simulations Insights.

Ethylcellulose (EC) is a crucial cellulose derivative with widespread applications, particularly in the pharmaceutical industry, where precise property adjustments through chemical modification are imperative. The degree of substitution (DS) and the localization of substituents along the cellulose chains are pivotal factors in this process. However, the impact of the substituent location within the repeating unit of EC remains unexplored. To address this gap, we conducted molecular dynamics simulations on amorphous EC, comparing randomly and uniformly substituted ethyl groups in the repeating units. This comprehensive study of pairwise interactions revealed significant differences in intramolecular and intermolecular hydrogen-bonding capabilities, depending on whether the hydroxyl groups were substituted at C2, C3, or C6. While our simulations demonstrated that substituent localization in the repeating unit influenced the density, number of hydrogen bonds, and conformations, the DS emerged as the dominant determinant. This insight led us to propose and validate a hypothesis: a straightforward linear function using the properties of uniform models and molar fractions can predict the properties of randomly substituted EC with a given DS. This innovative approach is anticipated to contribute to the selection of cellulose derivatives with desirable properties for the pharmaceutical industry and new applications in other fields.

Thermodynamic parameters of L-carnosine dissolution and solvation in binary aqueous-organic mixtures at T = 298.15 K.

Isothermal calorimetry was used to measure the enthalpies of L-carnosine digestion in aqueous solutions of acetonitrile (AN) 1,4-dioxane (DO), acetone (AC), dimethyl sulfoxide (DMSO), ethanol EtOH), 1-propanol (1-PrOH), and 2-propanol (2-PrOH). The values of ΔsolH° obtained were combined with the standard enthalpies of L-carnosine sublimation to obtain the corresponding enthalpies of solvation ΔsolvH°. In addition, the enthalpic coefficients of pairwise interactions (hxy) of L-carnosine with organic solvent molecules and the enthalpy of transfer (ΔtrH°) from water in their mixture with the latter were calculated. It is established that the solvation of L-carnosine is weakened in a series of (H2O + EtOH) > (H2O + 1-PrOH) > (H2O + 2-PrOH). This may be due to an increase in the hydrophobicity of alcohols of the same order as well as the presence of CH3-groups within the alcohol molecule. The weakening of the solvation energy of L-carnosine by co-solvent molecules in a series of mixed solvents: (H2O + AN) < (H2O + AC) < (H2O + DO) < (H2O + DMSO) is associated both with an increase in the hydration energy of co-solvents in the same order and with their physicochemical properties. The enthalpic coefficients of water-organic solvent pairwise interactions were discovered to be linearly related to the corresponding coefficients of L-carnosine-organic solvent interactions. An increase in the polarity and acidity of an organic solvent increases the energy of pairwise interactions between L-carnosine and the organic solvent. Increasing the cohesion energy density, molar volume, and basicity of the organic solvent reduces the energy of the pairwise interactions between L-carnosine and the organic solvent.

Identifying vital nodes through augmented random walks on higher-order networks

Empirical networks possess considerable heterogeneity of node connections, resulting in a small portion of nodes playing crucial roles in network structure and function. Yet, how to characterize nodes' influence and identify vital nodes is by far still unclear in the study of networks with higher-order interactions. In this paper, we introduce a multi-order graph obtained by incorporating the higher-order bipartite graph and the classical pairwise graph and design a Higher-order Augmented Random Walk (HoRW) model through random walking on the proposed multi-order graph. This representation preserves as much information about the higher-interacting network as possible. In contrast to random walks along pairwise interactions only, performing more walks along the multi-order graph assists in not only identifying the most important nodes but also distinguishing nodes that ranked in the middle and bottom. Numerical results indicate that the proposed method effectively addresses the localization problem of certain classical centralities. Moreover, our method outperforms classical centralities in identifying vital nodes and can scale to various network tasks, including information spread maximization and network dismantling problems. Generally, the proposed higher-order representation and the random walk model provide novel insights and potent tools for studying higher-order mechanisms and functionality.

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells.

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Reconstructing higher-order interactions in coupled dynamical systems

Higher-order interactions play a key role for the operation and function of a complex system. However, how to identify them is still an open problem. Here, we propose a method to fully reconstruct the structural connectivity of a system of coupled dynamical units, identifying both pairwise and higher-order interactions from the system time evolution. Our method works for any dynamics, and allows the reconstruction of both hypergraphs and simplicial complexes, either undirected or directed, unweighted or weighted. With two concrete applications, we show how the method can help understanding the complexity of bacterial systems, or the microscopic mechanisms of interaction underlying coupled chaotic oscillators.

Prenatal exposure to polycyclic aromatic hydrocarbons and executive functions at school age: Results from a combined cohort study

BackgroundExecutive functions develop rapidly in childhood, enabling problem-solving, focused attention, and planning. Exposures to environmental toxicants in pregnancy may impair healthy executive function development in children. There is increasing concern regarding polycyclic aromatic hydrocarbons (PAHs) given their ability to transfer across the placenta and the fetal blood-brain barrier, yet evidence from epidemiological studies is limited. MethodsWe examined associations between prenatal PAH exposure and executive functions in 814 children of non-smoking mothers from two U.S. cohorts in the ECHO-PATHWAYS Consortium. Seven mono-hydroxylated PAH metabolites were measured in mid-pregnancy urine and analyzed individually and as mixtures. Three executive function domains were measured at age 8–9: cognitive flexibility, working memory, and inhibitory control. A composite score quantifying overall performance was further calculated. We fitted linear regressions adjusted for socio-demographics, maternal health behaviors, and psychological measures, and examined modification by child sex and stressful life events in pregnancy. Bayesian kernel machine regression was performed to estimate the interactive and overall effects of the PAH mixture. ResultsThe results from primary analysis of linear regressions were generally null, and no modification by child sex or maternal stress was indicated. Mixture analyses suggested several pairwise interactions between individual PAH metabolites in varied directions on working memory, particularly interactions between 2/3/9-FLUO and other PAH metabolites, but no overall or individual effects were evident. ConclusionWe conducted a novel exploration of PAH-executive functions association in a large, combined sample from two cohorts. Although findings were predominantly null, the study carries important implications for future research and contributes to evolving science regarding developmental origins of diseases.

Psychology-Guided Environment Aware Network for Discovering Social Interaction Groups from Videos

Social interaction is a common phenomenon in human societies. Different from discovering groups based on the similarity of individuals’ actions, social interaction focuses more on the mutual influence between people. Although people can easily judge whether or not there are social interactions in a real-world scene, it is difficult for an intelligent system to discover social interactions. Initiating and concluding social interactions are greatly influenced by an individual’s social cognition and the surrounding environment, which are closely related to psychology. Thus, converting the psychological factors that impact social interactions into quantifiable visual representations and creating a model for interaction relationships poses a significant challenge. To this end, we propose a Psychology-Guided Environment Aware Network (PEAN) that models social interaction among people in videos using supervised learning. Specifically, we divide the surrounding environment into scene-aware visual-based and human-aware visual-based descriptions. For the scene-aware visual clue, we utilize 3D features as global visual representations. For the human-aware visual clue, we consider instance-based location and behaviour-related visual representations to map human-centred interaction elements in social psychology: distance, openness, and orientation. In addition, we design an environment aware mechanism to integrate features from visual clues, with a Transformer to explore the relation between individuals and construct pairwise interaction strength features. The interaction intensity matrix reflecting the mutual nature of the interaction is obtained by processing the interaction strength features with the interaction discovery module. An interaction constrained loss function composed of interaction critical loss function and smooth F β loss function is proposed to optimize the whole framework to improve the distinction of the interaction matrix and alleviate class imbalance caused by pairwise interaction sparsity. Given the diversity of real-world interactions, we collect a new dataset named Social Basketball Activity Dataset (Soical-BAD), covering complex social interactions. Our method achieves the best performance among social-CAD, social-BAD, and their combined dataset named Video Social Interaction Dataset (VSID).

BMI modifies the effect of pregnancy complications on risk of small- or large-for-gestational-age newborns.

Maternal physical condition (reflected by maternal body mass index (BMI) at delivery) and pregnancy complications influence neonatal health outcomes. High BMI during pregnancy increases various health problems' risks, but studies about the synthesized effect of these factors on fetal growth, are scarce. The retrospective cohort study was conducted in Zhejiang Province, China from 1 January 2019 to 31 December 2021. The associations between complications and small-for-gestational-age (SGA) and large-for-gestational-age (LGA) were measured by the Fine-Gray model and subgroup analysis. Effect modification and interaction analyses were conducted to explore BMI's modification effect and complications' interaction. Several complications increased the risk for SGA and LGA, some significance varied in different subgroups. There was a positive effect modification of gestational diabetes mellitus (GDM) across BMI strata on LGA (relative excess risk due to interaction (RERI) [95% CI] = 0.57 [0.09,1.04]). Several pairwise complications' interactions were synergistic (e.g., pregestational diabetes and intraamniotic infection for SGA (ratio of ORs [95% CI] = 8.50 [1.74,41.37]), pregestational diabetes and assisted reproductive technology (ART) for LGA (ratio of ORs [95% CI] = 2.71 [1.11,6.62])), one was antagonistic (placental problems and ART for LGA (ratio of ORs [95% CI] = 0.58 [0.35,0.96])). High-BMI positively modified the risk of GDM on LGA. Many interactions existed when two specific pregnancy complications occurred simultaneously. This is the largest retrospective study covering more than 10 pregnancy complications to date in this aspect. High-BMI (BMI > 28 kg/m2) positively modifies the risk of GDM on LGA. Many pregnancy complications influence the risk of SGA and LGA, with several interactions that may create a "syndrome" effect. Pregnant women with different BMIs should consider the additional risks caused by pregnancy complications for their heterogeneous effects on abnormal fetal growth. Measures should be taken to prevent the occurrence of other exposure factors in the "syndrome". This study may aid in developing a new strategy for improving neonatal outcomes.

Abstract A023: Determining genetic interaction from double knockout CRISPR screening

Abstract Background For decades double knockout (KO) perturbation screens were limited to model organisms such as S. pombe and S. cerevisiae. CRISPR technology has revolutionized genetic interaction discovery by allowing large scale screening in human cell lines, organoids, and mouse models. However, there remains much uncertainty regarding the optimal way to determine the presence of genetic interaction from the raw data generated from these large scale double perturbation experiments. Here we compare two different analysis methods run on the same normalized dataset to determine to what degree does the analysis method influence the determination of genetic interaction. Methods A publicly available genetic interaction dataset containing 24,908 double knock-out constructs across three cell lines (Hela, A549, 293T) in four time points (day 3, 14, 21, 28) and two replicates generated from a pair-wise CRISPR-Cas9 KO screen was used for analysis (Shen et al, Nature Methods, 2017). These data were used to measure single gene fitness scores for 73 known cancer driver genes and all 2628 pair-wise interactions using (1) the numerical Bayesian method from Shen et al, called CTG (Compositional and Time-course-aware Genetic analysis), and (2) the variational Bayesian method GEMINI (Zamanighomi et al, Genome Biology, 2019). Results Single gene KO fitness measurements from CTG and GEMINI were highly correlated for all three cell lines (pearson r 0.678, 0.604, 0.784 for HeLa, A549, and 293T, respectively; p&amp;lt; 0.1 x10-8 for each). In contrast, correlation of genetic interaction scores between the two methods was essentially random: HeLa r= -0.0143, p= 0.47, A549; A549 r= -0.0476, p=0.015; 293T r= -0.0135, p= 0.49. Of 52 synthetic lethal interactions identified by CTG in HeLa at z-score cut off -3, none were identified by GEMINI at same Z cutoff. Conversely of 4 interactions identified by GEMINI, none were identified by CTG. Similarly in A549, of 57 interactions identified by CTG none were identified by GEMINI, of 3 interactions identified by GEMINI none were identified by CTG. Restricting to genetic interactions that were validated in low-throughput drug-drug assays, of 5 synthetic lethal interactions found in HeLa by CTG (CHEK1-MAP2K1, CHEK1-TYMS, ADA-CHEK1, ATM-CHEK1, CDK9-CHEK1) all but CHEK1-TYMS were validated in low-throughput assays. However none of the 5 were scored as hits by GEMINI. Of 3 interactions scored as synthetic lethal in A549 (PRKDC-RRM2, CDK9-PRKDC, CDK4-PRKDC) all but PRKDC-RRM2 were validated, none of the 3 were scored as hits by GEMINI. Conclusions This study highlights dramatic differences in calculated genetic interaction scores from two different computational algorithms applied to the same experimental data. With only 8 of 2628 (0.3%) interactions tested in validation experiments it is not currently possible to know the ground truth in order to assess which method is most accurate. The generation of synthetic genetic interaction data will be an important step for further optimization of algorithms to detect genetic interaction. Citation Format: John Paul Shen, Yue Gu, Saikat Chowdhury. Determining genetic interaction from double knockout CRISPR screening [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A023.

Evolutionary Game Dynamics with Environmental Feedback in a Network with Two Communities

Recent developments of eco-evolutionary models have shown that evolving feedbacks between behavioral strategies and the environment of game interactions, leading to changes in the underlying payoff matrix, can impact the underlying population dynamics in various manners. We propose and analyze an eco-evolutionary game dynamics model on a network with two communities such that players interact with other players in the same community and those in the opposite community at different rates. In our model, we consider two-person matrix games with pairwise interactions occurring on individual edges and assume that the environmental state depends on edges rather than on nodes or being globally shared in the population. We analytically determine the equilibria and their stability under a symmetric population structure assumption, and we also numerically study the replicator dynamics of the general model. The model shows rich dynamical behavior, such as multiple transcritical bifurcations, multistability, and anti-synchronous oscillations. Our work offers insights into understanding how the presence of community structure impacts the eco-evolutionary dynamics within and between niches.

Magnetic Dirac semimetal state of (Mn,Ge)Bi2Te4

The ability to finely tune the properties of magnetic topological insulators (TIs) is crucial for quantum electronics. We studied solid solutions with a general formula GexMn1-xBi2Te4 between two isostructural Z2 TIs, magnetic MnBi2Te4 and nonmagnetic GeBi2Te4 with Z2 invariants of 1;000 and 1;001, respectively. We observed linear x-dependent magnetic properties, composition-independent pairwise exchange interactions, and topological phase transitions (TPTs) between topologically nontrivial phases and the semimetal state. The TPTs are driven purely by the variation of orbital contributions. By tracing the x-dependent Bi 6p contribution to the states near the fundamental gap, the effective spin-orbit coupling variation is extracted. The gapless state observed at x = 0.42 closely resembles a Dirac semimetal above the Néel temperature and shows a magnetic gap below, which is clearly visible in raw photoemission data. The observed behavior demonstrates an ability to precisely control topological and magnetic properties of TIs.

Composition of the Water Dimer and the Heterodimers of Water with N2 and O2 in Earth's Atmosphere.

The mole fractions χ and number concentrations n of the water dimer and the heterodimers H2O-N2 and H2O-O2 in Earth's atmosphere are reported up to 20 km. The water dimer data is obtained from published values of the equilibrium constant based on the water equation of state. The mixed equilibrium constants for the heterodimers are obtained from the respective second virial coefficients using an approach introduced by Stogryn and Hirschfelder that extracts the components pertaining to pairwise interactions producing bound and metastable dimers. From these calculations, χ and n for the water dimer and the (H2O)(N2) and (H2O)(O2) heterodimers at standard sea level are 1.79(6) × 10-5, 4.77(12) × 10-5 and 9.90(5) × 10-6 and 4.55(15) × 1014, 1.23(3) × 1016 and 2.56(1) × 1015, respectively. Analytical expressions are provided for these quantities for altitudes between 0-20 km and temperatures from 200-300 K. Sea level values of χ and n are given for two specific locations.

Higher-order correlations reveal complex memory in temporal hypergraphs

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.

Triadic interaction in the background of a pairwise spin-glass.

Developing an equilibrium solution for a pairwise spin-glass with a quenched random infinite range shows a continuous phase transition. Models with p-spin interactions have been studied and the exact solution was provided that shows a continuous phase transition for p=2 and a first-order one for p>2. Although the p-spin interactions were studied individually without considering lower-order interactions, is it always feasible to ignore the lower ones? Here, we are interested in finding an analytical solution for considering a triadic interaction as a perturbation in the background of a pairwise interaction in the Sherrington-Kirkpatrick spin-glass model. Our results indicate a sudden phase transition as a consequence of considering triadic interactions that signal a switch from a continuous to an explosive phase transition.