Pair Interactions Research Articles

Characterization of ligand-receptor pair in acute myeloid leukemia: a scoring model for prognosis, therapeutic response, and T cell dysfunction.

The significance of ligand-receptor (LR) pair interactions in the progression of acute myeloid leukemia (AML) has been the focus of numerous studies. However, the relationship between LR pairs and the prognosis of AML, as well as their impact on treatment outcomes, is not fully elucidated. Leveraging data from the TCGA-LAML cohort, we mapped out the LR pair interactions and distinguished specific molecular subtypes, with each displaying distinct biological characteristics. These subtypes exhibited varying mutation landscapes, pathway characteristics, and immune infiltration levels. Further insight into the immune microenvironment among these subtypes revealed disparities in immune cell abundance. Notably, one subtype showed a higher prevalence of CD8 T cells and plasma cells, suggesting increased adaptive immune activities. Leveraging a multivariate Lasso regression, we formulated an LR pair-based scoring model, termed "LR.score," to classify patients based on prognostic risk. Our findings underscored the association between elevated LR scores and T-cell dysfunction in AML. This connection highlights the LR score's potential as both a prognostic marker and a guide for personalized therapeutic interventions. Moreover, our LR.score revealed substantial survival prediction capacities in an independent AML cohort. We highlighted CLEC11A, ICAM4, ITGA4, and AVP as notably AML-specific. qRT-PCR analysis on AML versus normal bone marrow samples confirmed the significant downregulation of CLEC11A, ITGA4, ICAM4, and AVP in AML, suggesting their inverse biomarker potential in AML. In summary, this study illuminates the significance of the LR pair network in predicting AML prognosis, offering avenues for more precise treatment strategies tailored to individual patient profiles.

BOS and Hot-Film Analysis of a CH-53G Helicopter Wake in Ground Effect

Constant temperature anemometry and background-oriented schlieren (BOS) measurements on a CH-53G helicopter (takeoff mass ≈ 14t) are used to investigate the rotor wake including the vortex breakdown to statistical turbulence. Hovering flights at a range of hub heights 0.50 < h < 0.83 were investigated. The position of the rotorcraft was optically tracked using markers affixed to the fuselage. A complete breakdown of the tip vortices was noted within 0.3R of the rotor plane, in stark qualitative contrast to small-scale experiments which show the tip vortices persisting to the ground plane. The large (37 m2) BOS field of view showed a small effect of vortex pairing and blade vortex interaction, which was overshadowed by the rapid wake breakdown. Vortex pairing was found to result from varying vortex convection velocities rather than from initial differences in the vortex spacing due to the blade track.

Cybloids - creation and control of cybernetic colloids.

Colloids play an important role in fundamental science as well as in nature and technology. They have had a strong impact on the fundamental understanding of statistical physics. For example, colloids have helped to obtain a better understanding of collective phenomena, ranging from phase transitions and glass formation to the swarming of active Brownian particles. Yet the success of colloidal systems hinges crucially on the specific physical and chemical properties of the colloidal particles, i.e. particles with the appropriate characteristics must be available. Here we present an idea to create particles with freely selectable properties. The properties might depend, for example, on the presence of other particles (hence mimicking specific pair or many-body interactions), previous configurations (hence introducing some memory or feedback), or a directional bias (hence changing the dynamics). Without directly interfering with the sample, each particle is fully controlled and can receive external commands through a predefined algorithm that can take into account any input parameters. This is realized with computer-controlled colloids, which we term cybloids - short for cybernetic colloids. The potential of cybloids is illustrated by programming a time-delayed external potential acting on a single colloid and interaction potentials for many colloids. Both an attractive harmonic potential and an annular potential are implemented. For a single particle, this programming can cause subdiffusive behavior or lend activity. For many colloids, the programmed interaction potential allows to select a crystal structure at wish. Beyond these examples, we discuss further opportunities which cybloids offer.

A Siamese Neural Network Framework With Sememe-Based Context Extraction for Interactive Argument Pair Identification

A Siamese Neural Network Framework With Sememe-Based Context Extraction for Interactive Argument Pair Identification

Modelling structure and ionic diffusion in a class of ionic liquid crystal-based solid electrolytes.

Next-generation high-efficiency Li-ion batteries require an electrolyte that is both safe and thermally stable. A possible choice for high performance all-solid-state Li-ion batteries is a liquid crystal, which possesses properties in-between crystalline solids and isotropic liquids. By employing molecular dynamics simulations together with various experimental techniques, we have designed and analyzed a novel liquid crystal electrolyte composed of rigid naphthalene-based moieties as mesogenic units, grafted to flexible alkyl chains of different lengths. We have synthesized novel highly ordered lamellar phase liquid crystal electrolytes at 99% purity and have evaluated the effect of alkyl chain length variation on ionic conduction. We find that the conductivity of the liquid crystal electrolytes is directly dependent on the extent of the nanochannels formed by molecule self-organization, which itself depends non-monotonously on the size of the alkyl chains. In addition, we show that the ion pair interaction between the anionic center of the liquid crystal molecules and the Li+ ions plays a crucial role in the overall conductivity. Based on our results, we suggest that further improvement of the ionic conductivity performance is possible, making this novel family of liquid crystal electrolytes a promising option for the design of entirely solid-state Li+ ion batteries.

Pharmacogenomics and Big Data in medical oncology: developments and challenges.

Medical oncology, through conventional chemotherapy as well as targeted drugs, remains an important component of cancer patient management, particularly for systemic disease. Despite advances in all areas of medical oncology, certain challenges persist in the form of drug resistance and severe normal tissue toxicity. These unwanted effects can be counteracted through a patient-tailored treatment approach, which in chemotherapy is translated as pharmacogenomics. This research field investigates the way genetic makeup influences a patient's response to various drugs with the aim to minimize trial-and-error associated with drug administration. The paper introduces the role, advances and challenges of pharmacogenomics, highlighting the importance of Big Data mining to reveal the mechanisms behind drug-gene pair interaction for better patient outcomes. International consortiums have prioritized their focus on the clinical implementation of pharmacogenomics while tackling the challenges ahead: data standardization, ethical aspects and the education of physicians and patients alike to comprehend the power of pharmacogenomics to transform medical oncology.

Solvation dynamics on the diffusion timescale elucidated using energy-represented dynamics theory.

Photoexcitation of a solute alters the solute-solvent interaction, resulting in the nonequilibrium relaxation of the solvation structure, often called a dynamic Stokes shift or solvation dynamics. Thanks to the local nature of the solute-solvent interaction, the characteristics of the local solvent environment dissolving the solute can be captured by the observation of this process. Recently, we derived the energy-represented Smoluchowski-Vlasov (ERSV) equation, a diffusion equation for molecular liquids, which can be used to analyze the solvation dynamics on the diffusion timescale. This equation expresses the time development for the solvent distribution on the solute-solvent pair interaction energy (energy coordinate). Since the energy coordinate can effectively treat the solvent flexibility in addition to the position and orientation, the ERSV equation can be utilized in various solvent systems. Here, we apply the ERSV equation to the solvation dynamics of 6-propionyl-2-dimethylamino naphthalene (Prodan) in water and different alcohol solvents (methanol, ethanol, and 1-propanol) for clarifying the differences of the relaxation processes among these solvents. Prodan is a solvent-sensitive fluorescent probe and is thus widely utilized for investigating heterogeneous environments. On the long timescale, the ERSV equation satisfactorily reproduces the relaxation time correlation functions obtained from the molecular dynamics (MD) simulations for these solvents. We reveal that the relaxation time coefficient on the diffusion timescale linearly correlates with the inverse of the translational diffusion coefficients for the alcohol solvents because of the Prodan-solvent energy distributions among the alcohols. In the case of water, the time coefficient deviates from the linear relationship for the alcohols due to the difference in the extent of importance of the collective motion between the water and alcohol solvents.

The effect of structural changes on the self-assembly of novel green pyridinium-carboxylate gemini surfactants in Langmuir and Langmuir-Blodgett films.

Engineered molecules with tailored molecular structures have the potential to advance various disciplines by enhancing the properties of biological membranes. In this study, we investigated the fundamental interfacial behavior of newly synthesized, water insoluble, cationic pyridinium-carboxylate based gemini surfactants (GSs) using picolinic acid (PA), nicotinic acid (NA), and isonicotinic acid (INA) and their interactions with dipalmitoylphosphatidylcholine (DPPC) in Langmuir and Langmuir-Blodgett (LB) films. Two synthetic methodologies were employed: (a) connecting two alkyl pyridinecarboxylates through the nitrogen atoms with a xylenyl spacer, namely, PAGS, NAGS1, and INAGS; and (b) dimerizing two nicotinic acid molecules through ester linkages with 1,4-benzenedimethanol, and then quaternizing the pyridine nitrogens with hexadecyl chains to yield NAGS2. A combination of Brewster angle microscopy (BAM) and atomic force microscopy (AFM) imaging techniques yielded valuable insights into the morphology of the GS films and their mixtures with DPPC. Density functional theory (DFT) calculations were used to gain further information on the GSs structures and understand their assembly. The results indicate that the film of INAGS is the most hydrophobic film, and its monolayer is the least compressible. When the nitrogen atom and a carboxylate group of the headgroup are positioned closer to each other, the GS molecules tend to form aggregates instead of a continuous film which is observed for the INAGS surfactant. This observation is consistent with the DFT energy values of pair interactions, indicating that both PAGS and NAGS1 have closely packed conformations with high stabilization energy.

Computational investigation of the effects of polymer grafting on the effective interaction between silica nanoparticles in water.

Understanding and control of the effective interaction between nanoscale building blocks (colloids or nanoparticles) dispersed in a solvent is an important prerequisite for the development of bottom-up design strategies for soft functional materials. Here, we have employed all-atom molecular dynamics simulations to investigate the impact of polymer grafting on the solvent-mediated effective interaction between the silica nanoparticles (Si-NPs) in water, and in turn, on its bulk structural and thermodynamic properties. We found that the nature of the short grafting polymers [characterized by their interaction with water (hydrophobicity or hydrophilicity) and molecular weight] has a profound effect on the range and strength of the effective interaction between the Si-NPs. The hydrophobic polymer [such as polyethylene (PE)]-grafting of Si-NP gives rise to a more attractive interaction between the Si-NPs compared to the hydrophilic polymer [such as polyethylene glycol (PEG)] and non-grafted cases. This study further provides fundamental insights into the molecular origin of the observed behavior of the effective pair interactions between the grafted Si-NPs. For PE-grafted Si-NPs, the confined water (water inside the cavity formed by a pair of Si-NPs) undergoes a partial dewetting transition on approaching below a critical inter-particle separation leading to a stronger attractive interaction. Furthermore, we report that the effective attraction between the PE-grafted Si-NPs can be reliably controlled by changing the grafting PE density. We have also investigated the bulk structural and thermodynamic behavior of the coarse-grained Si-NP system where the particles interact via effective interaction in the absence of water. We believe that the insights gained from this work are important prerequisites for formulating rational bottom-up design strategies for functional materials where nano- (or, colloidal) particles are the building blocks.

Swimmers at interfaces enhance interfacial transport.

The behavior of fluid interfaces far from equilibrium plays central roles in nature and in industry. Active swimmers trapped at interfaces can alter transport at fluid boundaries with far reaching implications. Swimmers can become trapped at interfaces in diverse configurations and swim persistently in these surface adhered states. The self-propelled motion of bacteria makes them ideal model swimmers to understand such effects. We have recently characterized the swimming of interfacially trapped Pseudomonas aeruginosa PA01 moving in pusher mode. The swimmers adsorb at the interface with pinned contact lines, which fix the angle of the cell body at the interface and constrain their motion. Thus, swimmers become trapped at interfaces in diverse configurations and swim persistently in these surface adhered states. We observe that most interfacially trapped bacteria swim along circular paths. Fluid interfaces also typically form incompressible two-dimensional layers. These effects influence the flow generated by the swimmers. In our previous work, we have visualized the interfacial flow around a pusher bacterium and described the flow field using two dipolar hydrodynamic modes; one stresslet mode whose symmetries differ from those in bulk, and another bulk mode unique to incompressible fluid interfaces. Based on this understanding, swimmer-induced tracer displacements and swimmer-swimmer pair interactions are explored using analysis and experiment. The settings in which multiple interfacial swimmers with circular motion can significantly enhance interfacial transport of tracers or promote mixing of other swimmers on the interface are identified through simulations and compared to experiment. This study shows the importance of biomixing by swimmers at fluid interfaces and identifies important factors in the design of biomimetic active colloids to enhance interfacial transport.

Study on Thermodynamic Properties and Elastic Deformation of Full-Heusler Alloys Co2TaGa and Co2VGa by Statistical Moment Method

Heusler alloy is a ferromagnetic alloy based on the Heusler phase. It is an intermetallic alloy between metallic and non-metallic elements that can be non-ferromagnetic and has a BCC crystal structure. This paper presents the theory of thermodynamics and elastic deformation of full-Heusler A2BC alloy with BCC structure built on the basis of statistical moment method (SMM). The paper presents numerical results for the temperature and pressure dependence of the mean nearest neighbor distance between two atoms, the density, the volume, the thermal expansion coefficient, the isotherm and adiabatic compressibilities, the isothermal elastic modulus, the heat capacities at constant volume and pressure, Young’s modulus, the bulk modulus, longitudinal and transverse wave velocities of alloys Co2TaGa and Co2VGa when using the coordination sphere method and the Mie–Lennard-Jones pair interaction potential. Some SMM calculation results agree well with available ab initio calculation results.

Host‐, Environment‐, or Human‐Related Effects Drive Interspecies Interactions in an Animal Tuberculosis Multi‐Host Community Depending on the Host and Season

In many Mediterranean ecosystems, animal tuberculosis (TB), caused by Mycobacterium bovis, is maintained by multi‐host communities in which cattle and different wildlife species establish interaction networks contributing to M. bovis transmission and persistence. Most studies have addressed wildlife–cattle disease‐relevant interactions, focusing on reservoir hosts, while disregarding the potential contribution of the so‐called accidental hosts and/or neglecting wildlife–wildlife interactions. In this work, we aimed to characterise interspecies interactions in an endemic TB risk area and identify the ecological drivers of interaction patterns regardless of the pre‐attributed role of host species on TB epidemiology. For that purpose, spatial–temporal indirect interactions between wildlife mammals and cattle, and between different wildlife species, were investigated through camera trapping. Second, five ecological hypotheses potentially driving species pair interactions in the wet and dry seasons were tested covering water and control sites: human presence (H1), landscape composition (H2), topography (H3), weather (H4), and natural food and water resources (H5). Wild boar (Sus scrofa), red deer (Cervus elaphus), and red fox (Vulpes vulpes) were the wildlife species mostly involved in indirect interactions. We found that indirect wildlife–cattle interactions were more frequent than wildlife interactions and, for certain species pairs, interaction rates were higher in the wet season in both wildlife–cattle and wildlife groups. Natural food and water resources (H5) was the most supported hypothesis that influenced the abundance of wildlife–cattle interactions, with positive effects during the dry season and negative effects during the wet season. In contrast, the abundance of indirect interactions between wildlife species was mainly supported by the human disturbance hypothesis (H1), with negative effects exerted on the dry season and variable effects on the wet season. Other tested hypotheses also influenced wildlife–cattle and wildlife–wildlife interactions, depending on the season and host species. These results highlight that indirect interactions, and thus conditions potentially favouring the transmission of M. bovis in shared environments, are determined by different ecological backgrounds.

How do water-mediated interactions and osmotic second virial coefficients vary with particle size?

We examine quantitatively the solute-size dependences of the effective interactions between nonpolar solutes in water and in a simple liquid. The potential w(r) of mean force and the osmotic second virial coefficients B are calculated with high accuracy from molecular dynamics simulations. As the solute diameter increases from methane's to C60's with the solute-solute and solute-solvent attractive interaction parameters fixed to those for the methane-methane and methane-water interactions, the first minimum of w(r) lowers from -1.1 to -4.7 in units of the thermal energy kT. Correspondingly, the magnitude of B (<0) increases proportional to σα with some power close to 6 or 7, which reinforces the solute-size dependence of B found earlier for a smaller range of σ [H. Naito, R. Okamoto, T. Sumi and K. Koga, J. Chem. Phys., 2022, 156, 221104]. We also demonstrate that the strength of the attractive interactions between solute and solvent molecules can qualitatively change the characteristics of the effective pair interaction between solute particles, both in water and in a simple liquid. If the solute-solvent attractive force is set to be weaker (stronger) than a threshold, the effective interaction becomes increasingly attractive (repulsive) with increasing solute size.

Does Interactive Conditioning Help Users Better Understand the Structure of Probabilistic Models?

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people are hesitant to use them. There is a need for tools to communicate probabilistic models more intuitively and help users build, validate, use effectively or trust probabilistic models. We focus on visual representations of probabilistic models and introduce the Interactive Pair Plot (IPP) for visualization of a model's uncertainty, a scatter plot matrix of a probabilistic model allowing interactive conditioning on the model's variables. We investigate whether the use of interactive conditioning in a scatter plot matrix of a model helps users better understand variables' relations. We conducted a user study and the findings suggest that improvements in the understanding of the interaction group are the most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations, in comparison to the understanding of the static group. As the detail of the inferred information increases, interactive conditioning does not lead to considerably longer response times. Finally, interactive conditioning improves participants' confidence about their responses.

Integrative approach for predicting drug-target interactions via matrix factorization and broad learning systems.

In the drug discovery process, time and costs are the most typical problems resulting from the experimental screening of drug-target interactions (DTIs). To address these limitations, many computational methods have been developed to achieve more accurate predictions. However, identifying DTIs mostly rely on separate learning tasks with drug and target features that neglect interaction representation between drugs and target. In addition, the lack of these relationships may lead to a greatly impaired performance on the prediction of DTIs. Aiming at capturing comprehensive drug-target representations and simplifying the network structure, we propose an integrative approach with a convolution broad learning system for the DTI prediction (ConvBLS-DTI) to reduce the impact of the data sparsity and incompleteness. First, given the lack of known interactions for the drug and target, the weighted K-nearest known neighbors (WKNKN) method was used as a preprocessing strategy for unknown drug-target pairs. Second, a neighborhood regularized logistic matrix factorization (NRLMF) was applied to extract features of updated drug-target interaction information, which focused more on the known interaction pair parties. Then, a broad learning network incorporating a convolutional neural network was established to predict DTIs, which can make classification more effective using a different perspective. Finally, based on the four benchmark datasets in three scenarios, the ConvBLS-DTI's overall performance out-performed some mainstream methods. The test results demonstrate that our model achieves improved prediction effect on the area under the receiver operating characteristic curve and the precision-recall curve.

TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction

Background and objectiveRecent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. MethodsTo address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. ResultsExperimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. ConclusionsIn this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.

Exclusion principle between the physicochemical properties of complementary nucleobases and symmetry breaking in double-stranded DNA conformations

The information contained in DNA is encoded in a double helix formed by two antiparallel and complementary sequences of nucleotides, which are stabilized by hydrogen bonds. At physiological conditions, each nucleobase possesses three main characteristics; molecular structure, functional group, and orientation in the double helix. These properties produce steric constraints that allow only complementary interactions between nucleobase pairs to form double or triple hydrogen bonds. In this work, a mathematical model is proposed to describe the pairing interactions between nucleobases according to their physicochemical properties (structure, functional group and orientation). The results suggest that stable hydrogen bonds derived from nucleobase interactions can be represented by a set of 4 hermitian matrices and a Clifford Algebra Cl(4,0). Moreover, it is shown that the allowed interactions between pairs of nucleobases enable the formulation of an exclusion principle between the states of the observables. Finally, the mathematical representation of the hydrogen bonding between complementary nucleobases suggests a preferential orientation (chirality), which could be associated with the major groove in the double helix structure of B-DNA.

Determination of Pair Interaction Parameters of Multicomponent Polymer Systems.

From the examples of three and four-component polymer-polymer systems characterized by amorphous separation, an original technique for determining the pair parameters of interaction between components based on the sorption isotherms of common solvent vapor, particularly water vapor, has been developed. The possibility of calculating thermodynamic characteristics of multicomponent polymer compositions with specific interactions of functional groups from experimentally obtained sorption isotherms is shown. An algorithm for calculating pair interaction parameters, estimating concentration dependences of chemical potential and Gibbs free energy of mixing, and predicting the phase state of polymer mixtures was presented for the first time for such systems. The technique was tested on the example of systems poly(N-vinylpyrrolidone) (PNVP)-polyethylene glycol (PEG), PNVP-PEG-Poly(acrylic acid) (PAA), poly(N-vinylcaprolactam) (PNVCL)-PEG, and polyvinyl alcohol (PVA)-PEG.

Temperature and size dependence of energy barrier for magnetic flips in L10 FePt nanoparticles: A theoretical study

L10 FePt nanoparticle is among the most promising materials for magnetic recording and nanomagnetic applications. In nanoparticles, the superparamagnetic effect, wherein the thermal fluctuation is comparable to the energy barrier for magnetic flips, strongly affects the stability of magnetic recording. The temperature dependence of the magnetic anisotropy constant in L10 FePt nanoparticles is a crucial factor for estimating the relaxation time of magnetic flips in a magnetic nanoparticle. However, comprehensive simulations of the atomic level for energy barrier for magnetic flips in the L10 FePt nanoparticles are lacking. This study entailed a simulation that quantitatively reproduced the size dependence of the Curie temperature of nanoparticles in the experimental studies. Moreover, the surface effect, wherein the magnetization decreases owing to the loss of magnetic exchange interaction pairs on the surface, was also clarified. The temperature and size dependences of energy barrier for the magnetic flips of L10 FePt nanoparticles were elucidated.

Self-Adhesive, Strong Antifouling, and Mechanically Reinforced Methacrylate Hyaluronic Acid Cross-Linked Carboxybetaine Zwitterionic Hydrogels.

Hyaluronic acid and zwitterionic hydrogels are soft materials with poor mechanical properties. The unique structures and physiological properties make them attractive candidates for ideal hydrogel dressings, but the crux of lacking satisfying mechanical strengths and adhesive properties is still pendent. In this study, the physical cross-linking of dipole-dipole interactions of zwitterionic pairs was utilized to enhance the mechanical properties of hydrogels. The hydrogels have been prepared by copolymerizing methacrylate hyaluronic (HAGMA) with carboxybetaine methacrylamide (CBMAA) (the mass ratio of [HAGMA]/[CBMAA] is 2:5, 1:5, 1:10, or 1:20), obtaining HA-CB2.5, HA-CB5.0, HA-CB10.0, or HA-CB20.0 hydrogel. Therein, the HA-CB20.0 hydrogel with a high CBMAA content can generate a strong dipole-dipole interaction to form internal physical cross-links, exhibit stretchability and low elastic modulus, and withstand 99% compressive deformation and cyclic compression under strain at 90%. Moreover, the HA-CB20.0 hydrogel is adhesive to diverse substrates, including skin, glass, stainless steel, and plastic. The synergistic effect of HAGMA and CBMAA shows strong anti-biofouling, high water absorption, biodegradability under hyaluronidase, and biocompatibility.