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Computing molecular thermodynamic properties is instrumental in multiple scientific disciplines, such as statistical physics, N-body simulations, and molecular docking. However, exact thermodynamic calculations are almost always not feasible. In this work, we introduce a versatile algorithm designed to rapidly compute the two-body partition function, its related thermodynamic properties, and the second virial coefficient for anisotropic nanoparticles and proteins under the rigid-body approximation. Our method involves constructing a quasi-regular grid in the 5D angular space between pairs of arbitrary objects and efficiently scanning the radial-angular space between the rigid molecules. Where available, we find excellent agreement with light and X-ray scattering experiments, as well as with Monte Carlo simulations. Our results suggest a correction to current coarse-grained protein force fields, and we further discover a new, counterintuitive effect of temperature on virial coefficients, caused by a population shift in angular space due to the dielectric response of water. Finally, the grid can serve as an interpolation table for N-body simulations, increasing their performance by orders of magnitude.

The development of biomaterials that mimic the extracellular matrix of the native tissue represents an exciting frontier for tissue engineering and regenerative medicine. Injectable hydrogels made of short, self-assembling peptides offer a promising platform for the delivery and directed differentiation of therapeutic stem cells. However, the rational design of peptide hydrogels remains a significant challenge in tissue engineering due to our lack of understanding of the molecular mechanisms that underlie self-assembly. Although these materials hold great promise, most computational design efforts have focused on studying how peptide sequence impacts aggregation propensity. While useful as an initial indicator of self-assembly, aggregation propensity can be misleading as it is nearly synonymous with hydrophobic precipitation, thus highlighting the need for more robust screening and design strategies. To address this limitation, we introduce a systematic approach to study self-assembly beyond aggregation via molecular dynamics for designing peptide hydrogels. Our approach introduces several new atomistic descriptors-end-to-end distance, π-π stacking interactions, and residue-specific contacts-derived from molecular dynamics simulations to capture the nuances of the sequence-dependent assembly. We additionally uncovered key interactions among hydrophobic, aromatic, and charged residues that reliably predict gel formation, enabling a more rational approach to hydrogel design. We apply these molecular features to successfully predict a previously undiscovered, yet robust self-assembling sequence, KYYYL. An analysis of variance (ANOVA) confirms that our parameters provide significant differences among sequences, whereas aggregation propensity failed to reject the null hypothesis. Finally, we establish the sensitivity of simulation parameters to ensure methodological rigor and enable future study expansion in peptide sequence space. Our findings reveal that amino acid selection and position influence self-assembly. Furthermore, we demonstrate the key interactions among varying residues that reliably predict gel formation, enabling a more rational approach to supramolecular hydrogel design.

Foodborne pathogen Listeria monocytogenes causes listeriosis, a rare but deadly condition. Internalin A (InlA) and Listeriolysin O (LLO), its main virulence factors, facilitate adhesion, invasion, intracellular survival, and intercellular spreading, making them interesting therapeutic targets. L. monocytogenes infections are becoming harder to treat because of antibiotic resistance; hence, flavonoids are being considered. An integrated in-silico technique was used to test plant-derived flavonoids' inhibitory efficacy against these proteins. For both targets, three modes of docking (HTVS, SP, and XP docking) were used for the preliminary screening from a library of 1,254 flavonoids. While CIDs 441667, 15126294, and 187808 showed favorable in-silico profiles for InlA with scores of -8.461, -7.578, and -7.521 kcal/mol, respectively, CIDs 441699, 443648, and 442868 showed the best affinity for LLO with values of -7.446, -5.991, and -5.852 kcal/mol, respectively. Admet analysis predicted the drug-likeness and safety characteristics of the compounds. Subsequently, the QM calculation was employed to examine the interaction of these compounds with the receptor, alongside their MEP and NBO characteristics. The selected ligands and the control ampicillin for both proteins were utilized to build protein-ligand complexes, subsequently assessed via a 100 ns molecular dynamics simulation. Subsequent post-simulation MM-GBSA, PCA and DCCM analysis of the trajectories evaluated their dynamic stability concerning InlA and LLO. CIDs 441667 and 15126294 for InlA, as well as CIDs 441699 and 443648 for LLO, have been identified as potential inhibitors, establishing a basis for future in vivo investigations and experimental validation.

Self-consistent field theory simulations of rod-coil diblock copolymers in slit confinement present significant numerical challenges due to sharp density gradients near hard walls. To rigorously resolve these systems utilizing the Gaussian and wormlike chain models, a hybrid spectral-compact finite difference scheme is developed on a non-uniform Chebyshev-Gauss-Lobatto grid. Shen's Chebyshev spectral method is employed for the flexible blocks. For the semiflexible blocks, a second-order upwind compact scheme together with an L-stable TR-BDF2 contour-stepping algorithm is adopted. This hybrid framework effectively suppresses spurious numerical oscillations. This unconditionally stable formulation strictly preserves propagator non-negativity and achieves up to a two-orders-of-magnitude speedup over uniform-grid implementations while maintaining linear spatial scaling. Simulations utilizing this advanced framework under neutral wall conditions reveal that the confining walls naturally induce preferential wetting of the semiflexible blocks at the impenetrable boundaries. As the incompressibility penalty increases, the compressible system progressively approaches the incompressible limit. For the selected physical parameters, decreasing the slit width induces a sequence of structural transitions from a smectic-C morphology with three internal periods (SC3) to morphologies with two and one internal periods (SC2 and SC1), and ultimately to a highly compressed smectic-P morphology (SP1). The equilibrium thickness of these confined structures deviates from exact integer multiples of the bulk spatial period. This deviation arises from the volume compensation associated with boundary depletion layers, together with adjustments in the molecular tilt angle and the degree of molecular interdigitation.

Carbon nanocones are unique carbon nanostructures with conical shapes and edge-dependent electronic properties. In this work, we studied the effect of hydrogen passivation on the thermal stability and electronic structure of carbon nanocones. The findings indicate that hydrogen passivation suppresses edge-related electronic states and increases the HOMO-LUMO energy gap from 1.524 eV to 2.395 eV. Density functional theory (DFT) calculations were carried out using the B3LYP/LANL2DZ method implemented in Gaussian software. Molecular dynamics (MD) simulations were also performed in QuantumATK with the ReaxFF_CH_2017 reactive force field. The simulations employed a time step of 0.25 fs and were continued for 20 ps.

The ability to predict future emotions for upcoming events (affective forecasting, AF) is crucial for mental health. This study compared the distinct and shared characteristics of AF in participants with high levels of autistic traits (AT, n = 76), social anhedonia (SA, n = 107), subthreshold depression (SD, n = 102), and 99 controls. Participants completed the laboratory-based and real-life AF tasks, and resting-state functional magnetic resonance imaging. Compared with controls, the AT group reported fewer mental simulation details in the laboratory-based task, and the SA group displayed less anticipated pleasure and fewer mental simulation details in both laboratory-based and real-life AF tasks, whereas the SD group showed relatively intact AF. The three subclinical groups showed larger AF bias than controls. Moreover, the three subclinical groups showed altered functional connectivity (FC) compared with controls, and the altered FC was associated with laboratory-based and real-life AF performance. These findings advance our understanding of early identification and prevention strategies of transdiagnostic populations.

Water confined in zeolite-templated carbons (ZTCs) exhibits properties fundamentally different from those of bulk liquid, with profound implications for energy storage, separation technologies, and catalysis. Despite the technological importance of water behavior in ZTC nanopores, molecular-level understanding remains limited. This work presents comprehensive molecular dynamics (MD) simulations investigating the structure, dynamics, and hydrogen bonding characteristics of water confined within faujasite-derived ZTC. Classical MD simulations were developed with validated force fields to characterize radial and spatial distribution functions, hydrogen bond networks and lifetimes, cluster size distributions, domain formation, translational and rotational dynamics, and velocity autocorrelation functions. Systematic comparison with bulk liquid water reveals confinement-induced modifications to tetrahedral hydrogen bonding networks, spatial organization into discrete domains, hydrogen bond dynamics, and transport properties. The three-dimensional hierarchical pore topology of ZTC creates unique confinement environments distinct from one-dimensional nanotubes or two-dimensional slit pores. These findings provide molecular-level insights essential for the rational design of ZTC-based materials for electrochemical energy storage, water desalination membranes, proton exchange systems, and aqueous-phase catalysis, thereby advancing fundamental understanding of water confinement in complex carbon nanostructures.

As a major component of air pollution in urban environments, dust particles are similar in size to pollen grains. When deposited on the stigma, dust may occupy the space for pollen deposition and reduce the adhesion of pollen, potentially leading to a decrease in plant female fitness. Unfortunately, to date, the relevant evidence remains scarce. A dust simulation experiment was conducted on 29 plant species at the Yan'an Botanical Garden. The effects of dust deposition on the stigmatic surfaces were examined using microscopy, and female fitness was compared between experimental (dust) and control (non-dust) groups. We demonstrated that dust significantly occupied stigma surfaces and absorbed stigma secretions. The dust simulation treatment significantly decreased the fruit and seed set but did not influence the fruit length or weight. Moreover, plants with wet or more exposed stigmas showed greater susceptibility to dust, evidenced by relatively lower fruit and seed set in both the non-dust and dust treatments. Dust significantly reduces the reproduction of plants by altering the microenvironment of the flower stigma, including absorbed stigma secretions and occupied stigma surfaces, and its principal effect is observed during the critical pre-fertilization phase. Although our study significantly advances the understanding of the harmful effects of pollutants on plant reproduction, much remains to be learned and the underlying mechanisms need to be investigated in the future.

Zingerone (Zin) exhibits multiple pharmacological properties, including anti-inflammatory, immunomodulatory, anxiolytic, anti-thrombotic, radioprotective, and antimicrobial activities. However, the therapeutic potential of Zin in coronary artery atherosclerotic heart disease (CHD) remains unexplored. This study aims to elucidate the role and underlying molecular mechanisms of Zin in CHD treatment. Putative Zin targets associated with CHD were identified through online database screening followed by functional enrichment analysis. Core target genes were screened using Protein-protein interaction (PPI) Network analysis, machine learning algorithms, molecular docking, and molecular dynamics simulations. Protein expression was examined using western blot. The levels of interleukin (IL)-6 and tumor necrosis factor-α (TNF-α) were detected using detection kits. The senescence of cells was analyzed using senescence-associated β-galactosidase (SA-β-gal) staining kits. Zin was predicted to target several core genes, including v-akt murine thymoma viral oncogene homolog 2 (AKT2), heat shock protein 90α family class B member 1 (HSP90AB1), nuclear receptor subfamily 3 group C member 1 (NR3C1), forkhead box O1 (FOXO1), and toll-like receptor 4 (TLR4), potentially contributing to the alleviation of CHD. Furthermore, molecular docking and molecular dynamics simulation predicted a stable binding interaction between Zin and HSP90AB1. Zin ameliorated ox-LDL-stimulated inflammation, an effect that was associated with downregulation of HSP90AB1. Furthermore, Zin alleviated ox-LDL-induced senescence and apoptosis in a manner correlated with reduced HSP90AB1 levels in vitro. Zin suppresses inflammation, senescence, and apoptosis of oxidized low-density lipoprotein-induced primary human coronary artery endothelial cells in a manner associated with HSP90AB1 silencing. These findings provide a foundation for the further development of Zin-based treatment strategies for CHD.

Under snowy weather conditions, factors such as road conditions and weather conditions significantly affect vehicle car-following behavior. Traditional car-following models struggle to accurately capture driving characteristics on slippery roads. To address this, this paper proposes a parameter calibration method for car-following models under snowy conditions, considering factors including road adhesion coefficient and visibility. Five classical car-following models are selected for analysis: the GM model, the Gipps model, the Intelligent Driver Model (IDM), the Wiedemann model, and the Full Velocity Difference Model (FVDM). A systematic analysis is conducted on the key parameters to be calibrated for each model in snowy environments. To overcome the poor adaptability and low accuracy of traditional calibration methods, an adaptive parameter calibration framework combining an Informer time series encoder and a physics-informed neural network is proposed. This method extracts features of snowy environments using the Informer time series encoder and achieves dynamic optimization of model parameters via the physics-informed neural network algorithm, making it applicable to multiple car-following models simultaneously. Validation results based on the NGSIM dataset and real vehicle test data under snowy conditions show that the proposed method improves calibration accuracy by 12.7% under snowy scenarios compared to the traditional genetic algorithm, and exhibits strong generalization capability across different car-following models. This research can provide fundamental models for traffic simulation systems and enhance simulation accuracy.

In this paper, a dynamic epidemic model of botnet attack propagation in scale-free networks is introduced based on the epidemic model. The proposed attack propagation model is based on the Susceptible-Exposure-Infected-Improved-Vaccinated-Recovery (SEIRVS) epidemic model. Here, an Intrusion Detection System (IDS) for botnet attack detection is also presented. This method is based on a combination of machine learning and metaheuristic algorithms, the Golden Ratio Optimization (GRO) algorithm, Bat Algorithm (BA), and K-Nearest Neighbor (KNN) algorithms named (GRO-BA-K-NN), which includes three steps: 1) preprocessing, 2) GRO feature selection 3) attack detection using BA-K-NN. The proposed IDS, using the three datasets BOT-IOT, UNSW-NB15, and NLS-KDD, and the dynamic behavior of the proposed model, is evaluated using the metric of the initial production ratio; evaluating the dynamic behavior of the model can be used to predict whether the infection spreads or stops. The evaluation results show that the epidemic model reduces the density of infected nodes and stops the spread of infection compared to other existing models. The simulation results show that the proposed IDS was able to detect attacks with accuracy (0.938, 0.931, and 0.928) and also reduced the false negative and false positive rates.

Gas evolution is a critical challenge in lithium-ion batteries that undermines their performance in the renewable energy transition. This study investigates the mechanism of dihydrogen (H2) elimination in lithium-ion batteries using comprehensive quantum chemical calculations. Investigation of Li-(H2O)n+ clusters (n = 1-6) confirms that a minimum of four water molecules (n = 4) completes the first solvation shell, establishing a tetrahedral coordination environment. Evaluation of different chemical environments reveals that halogenated and nitrated anions moderately promote H2 elimination from the Li-(H2O)4+ cluster yielding Gibbs free energies of approximately -100 and -107 kcal/mol, respectively. Conversely, sulfur-containing anions significantly enhance the elimination process, displaying highly favorable Gibbs free energies exceeding -220 kcal/mol. Specifically, conformational analysis of trifluoromethane sulfonimide-derived anions, TFS-CF3 and TFS-N(CH2CH2OCH3)2 identified them as exceptionally effective promoters of hydrogen elimination, with Gibbs free energies reaching -261 and -273 kcal/mol, respectively. The thermodynamic properties of dihydrogen elimination were systematically benchmarked using a multi-level density functional theory (DFT) approach. This encompassed local meta-GGA (MN15L), hybrid-GGA with and without dispersion corrections (B3LYP, B3LYP-D3, APFD, and ωB97X-D), hybrid meta-GGA (M06-2X, M08-HX, MN15, and TPSSh) and double-hybrid (B2PLYP-D3 and PBE-QIDH). Wave function-based methods (MP2, MP4, CCSD, and CCSD(T)) were carried out using the 6-311 +  + G(3d2f,3p2d) and 6-311 +  + G(3df,3pd) basis sets. Thermochemical benchmarking was performed using composite methods (G4MP2, W1BD, and G4). All simulations were conducted using the Gaussian 16 program. Binding energies were corrected for zero-point energy (ZPE) and basis set superposition error (BSSE). Further, Gibbs free energies were computed at 298 K to highlight the entropic contributions driving the elimination mechanism.