Browse Articles

Anisotropic compressibility in inhomogeneous fluids

Compressibility has a clear meaning in bulk liquids, but at the interface between two fluids, compression can alter local molecular packing differently along the surface normal and parallel to the interfacial plane. Here, we introduce a linear-response approach to measure this directional mechanical response in inhomogeneous fluids. The response has a scalar part that recovers the usual macroscopic compressibility and deviatoric components that reveal how compression is distributed between normal and lateral molecular environments. These fluctuation formulas provide a practical route to quantify interfacial softness and directional compressive modes directly from equilibrium simulations. We apply the method to the water/carbon tetrachloride interface in molecular dynamics simulations and show that the interface has a strong local anisotropic response together with a positive scalar surface excess response. The resulting interfacial softening can significantly modify the apparent compressive response of water confined by organic liquids at thicknesses relevant to thin films and multilamellar vesicles.

Induction of paraptotic cell death in breast cancer cells by an iron chelator involved activation of UPR/MAPK pathway and inactivation of PI3K/AKT/mTOR

Impact of electronic excitation on dissociation in high-temperature oxygen, nitrogen, and air mixtures

The role of molecular electronic excitation in the dissociation of O2, N2, and NO in oxygen, nitrogen, and air mixtures is studied using an electronic state-resolved kinetic model of N2–O2–Ar mixtures. The longstanding disagreement between ab initio O2(X3Σg−)–O and experimental O2–O dissociation rate coefficients is explained mechanistically for the first time, including a clear explanation of why this effect is only observed in collisions with O and not with O2 or Ar. Using electronic state-resolved simulations of a recent set of shock tube experiments, a simulated inference of the O2–O rate coefficient is computed and shown to reproduce all experimental data within uncertainty. The model is then validated using ground and excited electronic state measurements of N2 from several shock tube experiments. Next, the validated model is used to study how electronic excitation affects N2 dissociation more broadly, with the N2(A3Σu+) state being shown to significantly enhance N2 dissociation within both nitrogen and air mixtures. The role of excited electronic states in NO dissociation is evaluated and shown to be negligible, owing to the low dissociation rate coefficients of the excited states. Key experiments needed to further validate and constrain the model predictions are identified, with priority given to direct measurements of dissociation in N2–O2 mixtures, and high spatial and temporal resolution measurements of N2(A3Σu+) in the near-shock region. Effective rate coefficients incorporating the influence of electronic excitation are extracted from state-resolved calculations and are compared with existing rate expressions, providing guidance for the treatment of electronic excitation effects in hypersonic computational fluid dynamics codes.

“Cell-free extracts from probiotics modulate miR-21 and miR-155 and tumor suppressors PDCD4 and PTEN in breast cancer”

Topological control of singlet fission

Singlet fission is a multiple-exciton-generation process that could dramatically improve photovoltaic efficiencies or enable quantum-information processing. Because it usually occurs among distinct molecules, it relies on the energy-level alignment and aggregate structure of its host material. These dual requirements severely restrict the known singlet-fission materials (primarily acenes, rylenes, and carotenoids) to those that serendipitously adopt favorable aggregate structures and singlet/triplet energy-level alignment. Programmable DNA-scaffolded molecular networks decouple these constraints, tuning energy levels by modular chromophore selection while controlling the aggregate structure through DNA origami. However, a theoretical framework is needed to optimize the design. We introduce a topological framework based on simplicial complexes and Hodge theory that captures a fundamental feature of singlet fission: that the singlet states reside on vertices while triplet-pair states reside on edges, making singlet fission an inherently vertex-to-edge conversion. This insight shifts the focus from pair-interactions to collective network arrangements, whose control is a strength of DNA nanotechnology. We apply this framework to five lattice structures (linear, square, honeycomb, Kagomé, and Lieb) parameterized with reported pentacene, diketopyrrolopyrrole, and perylene diimide values. The lattice structure multiplies the singlet-fission efficiency, with the Kagomé lattice providing approximately a 2× enhancement. Three features explain this advantage: triangular 2-simplices with correlated multi-channel pathways, the highest edge/vertex ratio among all lattices studied, and flatband exciton localization near fission-active motifs. These predictions are testable by 2D electronic spectroscopy on DNA-scaffolded chromophore networks.

Adaptive feature fusion of ResNet50 and vision transformer for robust agricultural pest classification

A Jacobian-corrected minimum-mode following bias potential for hyperdynamics

Rare-event molecular dynamics simulations are limited by the separation between atomic and activated timescales. Hyperdynamics accelerates these processes via bias potentials while preserving correct transition kinetics. The minimum-mode following (MMF) formulation provides a ridge-based bias construction but typically employs an identity approximation for the Jacobian together with local force evaluation, which introduces inconsistency in high-dimensional systems. Here, we develop a Jacobian-corrected MMF (J-MMF) method that improves both accuracy and consistency. The method introduces a path-informed semi-analytical Jacobian based on the sequential evolution of minimum-mode vectors and an orthogonal projection that removes spurious force components along the reaction coordinate, without additional force evaluations. Benchmark simulations of adatom diffusion on the Cu(100) surface (1–201 mobile atoms) demonstrate improved accuracy and robust, largely size-independent acceleration compared with standard MMF and bond-boost methods. J-MMF provides a scalable and consistent framework for rare-event simulations in complex atomistic systems.

Tibial cortex transverse transport-derived small extracellular vesicles promote diabetic wound healing by enhancing angiogenesis and suppressing inflammation

Simulations of solvent effects on excited state dynamics of <i>p</i> -DAPA, a red single benzene-based fluorophore

Fluorescence-based techniques are widely used in basic science and medicine for detecting molecules and probing chemical reactions in vitro and in vivo. Red fluorophores are especially of interest due to their potential applicability for deep-tissue and whole-body imaging with low background interference. The smallest red fluorophore is the benzene-based para-diacetylphenylenediamine (p-DAPA), which has a fluorescence quantum yield of 0.06 and high brightness. However, the effect of solvent type on the photophysical properties of p-DAPA has not been extensively explored. We have employed classical force field dynamics and ab initio Born–Oppenheimer molecular dynamics calculations in an explicit solvent to explore the ground and excited state potential energy surfaces of p-DAPA. Our results show that p-DAPA exhibits stronger fluorescence in nonpolar solvents due to the appearance of additional nonradiative decay pathways in polar solvents. The type of solvent modulates the type and speed of decay pathways observed, while the overall character of the nonradiative state is maintained. This work provides insight into how p-DAPA might be adapted for biological imaging and new solvent environments.

Eggshell powder as a biomimetic alternative to biodentine in primary teeth pulpotomy: a comparative clinical and radiographic study

Abstract Eggshell powder (EP) has been suggested as a cost-effective pulpotomy material due to its high biocompatibility, remineralization potential, and ability to induce dentin bridge formation. This study compared the clinical and radiographic outcomes of EP, Biodentine, and mineral trioxide aggregate (MTA) when used as pulpotomy agents in primary molars. A randomized split-mouth clinical trial was performed on 30 children, each receiving pulpotomy in 3 primary molars assigned to Group I (EP), Group II (Biodentine), and Group III (MTA), with 30 teeth per group. Clinical evaluations were carried out at baseline, 3-, 6-, and 12-month. Cone-beam computed tomography (CBCT) scans were performed immediately postoperatively and at 12- month to evaluate root resorption and radiodensity changes. All groups demonstrated 100% clinical success within 3-month. At 12-month, Group I showed significantly higher rates of spontaneous pain, abnormal mobility, fistula, and internal and external root resorption ( p &lt; .05). CBCT analysis revealed significantly lower radiodensity values in Group I compared with Groups II and III. Group III (MTA) exhibited the most favorable radiographic outcomes, followed by Group II (Biodentine). EP achieved acceptable short-term results; however, its long-term performance was inferior to MTA and Biodentine. Clinical significance EP represents a low-cost, biocompatible, and environmentally sustainable biomaterial derived from recycled natural waste. Despite its eco-friendly and biomimetic advantages, its long-term clinical and radiographic performance remained inferior to Biodentine and MTA, which continue to be the more reliable pulpotomy materials for primary molars. Trial registration NCT05812053, “ComparativeEvaluation of Eggshell Powder in Primary Teeth Pulpotomy”, https://clinicaltrials.gov/ . Registered: 2023/04/13.

Dissociation line of tetrahydrofuran hydrates from <i>NPH</i> molecular dynamics simulations

In this work, we study via molecular dynamics simulations the dissociation temperature (T2) of the tetrahydrofuran (THF) hydrate. By employing the direct coexistence technique within the isenthalpic–isobaric (NPH) ensemble, we evaluated the T2 values at 100, 250, 500, and 1000 bar using the TIP4P/Ice water model and a rigid, planar TraPPE-UA force field for THF. This rigid and planar THF model based on the TraPPE-UA force field has demonstrated several times to yield identical results as the original and flexible TraPPE-UA model while significantly reducing the computational cost of the simulations. A key methodological aspect of this work is the transition from the traditional isothermal–isobaric (NPT) ensemble to the NPH ensemble to mitigate the stochastic inaccuracies and high computational costs of the hydrate dissociation temperature determination through the classical NPT + direct coexistence methodology. The dissociation temperatures, T2, obtained in this work at 100, 250, 500, and 1000 bar are 276.7(2), 274.2(2), 270.4(3), and 265.9(1) K, respectively. These results show an excellent agreement with existing experimental data and with NPT molecular dynamics simulation data previously reported in the literature. This study concludes that the NPH ensemble, combined with the direct coexistence technique, provides a robust, accurate, and computationally efficient framework for determining the dissociation boundaries of hydrate systems.

Machine learning-based prognosis and early death prediction in de novo stage IV breast cancer patients with bone metastasis: a SEER database and multicentre retrospective study

How the dielectric effect regulates energy conversion of nanofluidic pores

Electrokinetic energy conversion of electro-osmotic flow through charged nanoscale pores has attracted growing interest due to its promising potential in sustainable energy harvesting. This work focuses on dielectric regulation of energy conversion efficiency of electro-osmotic flow. To accurately describe the dielectric effect, we employ an energetic variational approach to develop a modified Poisson–Nernst–Planck–Navier–Stokes (mPNP-NS) framework that self-consistently incorporates ionic Born self-energy and inhomogeneous dielectrics due to the hydrophobicity of the pore wall. Lubrication–approximation analysis reveals that the leading-order solution to the mPNP-NS model satisfies the well-known Onsager reciprocal relations between fluxes and driving forces. Systematic numerical investigations demonstrate that tailored dielectric regulation can effectively enhance energy conversion efficiency through more favorable alignment between net ionic charge and large fluid velocity. Numerical studies also reveal a profound interplay between the dielectric profile, pore sizes, surface charge density, and bulk electrolyte concentration and their subtle synergistic impact on energy conversion efficiency. These findings highlight dielectric regulation as a new interfacial mechanism for boosting energy conversion efficiency and provide valuable insights on material selection and surface engineering for high-performance electrokinetic devices.

Decoding promoter activity from DNA sequence using pre-trained language models

Abstract Promoter architecture plays a central role in transcriptional regulation, but predicting promoter activity directly from DNA sequence remains challenging. Here, we tested whether transformer-based DNA language models can learn regulatory logic encoded in Drosophila core promoters. We fine-tuned the pretrained DNA language model DNABERT-2 using a synthetic core promoter dataset measured in S2 cells with luciferase reporter assays. The model predicted promoter activity well when biological replicates were split between training and test data (R² ≈ 0.91), and retained meaningful performance when test promoter sequences were fully excluded from training (R² ≈ 0.64). Model interpretation using SHapley Additive exPlanations (SHAP) 1 showed that predictive sequence features matched known promoter elements, including INR, TATA box, DRE, Ohler and MTE/DPE motifs, with position-dependent effects consistent with promoter architecture. Incorporating hormonal activation and nucleosomal context enabled sequence and biological context to be modeled in a unified framework. Gene-wise cross-validation showed promoter-specific generalization across most promoters with promoter-specific differences in accuracy. Applied without retraining to independent Drosophila embryo promoter data, the model captured partial in vivo activity trends. These results show that DNA language models can learn interpretable promoter sequence rules from controlled datasets, while accurate in vivo prediction will require broader regulatory context.

Rotational excitation of thioformaldehyde (H2CS) in collisions with molecular hydrogen

Thioformaldehyde (H2CS) has been observed in the interstellar medium (ISM). Accurate determination of column densities of the two nuclear spin modifications of H2CS in the low-density environment of the ISM ideally necessitates the use of a radiative transfer model. Such a model requires the availability of rate coefficients for inelastic rotational transitions in H2CS induced by collisions with the dominant hydrogen molecule in the ISM. To compute these rate coefficients, a potential energy surface (PES) for the interaction of H2CS with H2 is computed in this study by the explicitly correlated coupled cluster method including single, double, and (perturbatively) triple excitations [CCSD(T)-f12a] and a correlation-consistent aug-cc-pVTZ basis. The geometries of the molecules were fixed. The calculated points on the PES were fit to a functional form suitable for quantum scattering calculations. The well depth De of the H2CS–H2 PES was determined to equal 318.1 cm−1, and the equilibrium intermolecular separation was found to be 6.18a0. Time-independent quantum scattering close coupling calculations were performed to compute state-to-state cross sections and rate coefficients for transitions between the H2CS rotational levels induced by collisions with the hydrogen molecule.

Climate smart agricultural practices reduce technical inefficiency among smallholder vegetable farmers in Northwest Ethiopia

DNA conformation determines the size of DNA-histone H1 nanoscale clusters

Biological liquid–liquid phase separation has attracted considerable attention because of its essential biological functions. Numerous studies have been conducted to elucidate the molecular properties of host and client biomolecules in liquid condensates. However, most of the previous investigations have focused on well-grown micrometer-scale condensates, and consequently, information on small clusters with sizes smaller than the optical diffraction limit is very limited due to the lack of appropriate spectroscopic techniques. In this study, we apply two-dimensional fluorescence lifetime correlation spectroscopy and fluorescence lifetime correlation spectroscopy to elucidate the conformational properties of DNA in nanoscale DNA–histone H1 clusters. The results reveal that DNA and histone H1 form two distinct types of nanoscale clusters with different hydrodynamic radii, in which DNA adopts markedly different conformations. These findings suggest that DNA conformation plays a key role in determining the size of nanoscale clusters. Our results, therefore, provide important insights into the relationship between the growth of liquid condensates and the conformational states of host biomolecules.

Market trading method for hydrogen production and refueling integrated station operator under hydrogen sharing mechanism

Development of a magnetic interatomic potential for cubic antiferromagnets: The case of NiO

Interatomic potentials are essential for molecular dynamics simulations of magnetic materials, yet incorporating magnetic features into potentials for complex antiferromagnets remains challenging. Nickel oxide (NiO), a prototypical cubic antiferromagnet, exemplifies this difficulty. Here, we develop a methodology to integrate magnetic properties into interatomic potentials for cubic antiferromagnets by adding a magnetic Hamiltonian, which includes both the Heisenberg exchange and the Néel model. We apply this approach to NiO by constructing two potentials: one based on the Born model of ionic solids and another using a reference-free modified embedded atom method. The models are validated against density functional theory calculations and experimental data, showing excellent agreement in mechanical and magnetic properties across both zero and finite temperatures, correctly capturing thermal expansion and the temperature dependence of elastic constants. Furthermore, we demonstrate the sensitivity of the potential to symmetry-breaking lattice distortions (tetragonal, shear, and trigonal), providing a predictive framework for controlling the Néel vector via strain engineering in antiferromagnetic spintronics. These models enable large-scale simulations of magnetoelastic phenomena in antiferromagnets and open avenues for molecular dynamics studies involving coupled electric and magnetic fields in metal oxides.

Experimental and AI-based prediction of a solar air heater with novel recycled interlocking channel fins

Abstract Solar air heaters are a promising method used for drying and heating applications owing to their low operating costs and simple design. However, these solar air heaters exhibit limited thermal performance due to the low heat transfer coefficient between the absorber and the air passing through the duct, resulting in a decrease in thermal and exergy efficiency. The present work aims to address this issue by conducting a single-pass solar air heater with novel recycled aluminum interlocking channel fins, along with using artificial intelligence approaches to predict thermal and exergy efficiencies. The novelty of the present study is represented by integrating a comprehensive experimental assessment using the 4E analysis (Energy, Exergy, Economic, and Environmental analysis) with the development and comparison of artificial neural network (ANN) and deep neural network (DNN) models to predict efficiency under different operating conditions. Two solar air heaters were tested: a conventional (C-SAH) and a modified finned (M-SAH) heater, under natural and forced convection conditions at mass flow rates of 0.0046, 0.008, and 0.012 kg/s. The study findings reveal a significant enhancement in the modified heater. Under natural convection conditions, the outlet air temperature increased from 78 °C to 84 °C for the modified heater. Regarding thermal efficiency, the modified heater exhibited the highest efficiency of 48.6% at a flow rate of 0.012 kg/s. Daily thermal efficiency also increased from 32.91% to 44.36% at the same flow rate. The exergy efficiency reached a maximum of 2.80% for the modified heater. The AI models achieved high predictive performance; the DNN model achieved an R² of 0.924 for thermal efficiency, while the ANN model performed best in predicting exergy efficiency with an R² of 0.971. These results demonstrate the reliability of the proposed AI models in predicting the performance of SAHs. Additionally, the use of recycled aluminum fins significantly enhances the performance of SAHs, offering a low-cost and sustainable tool for solar air heating applications. The integration of artificial intelligence methods enhances the design and operation of highly efficient solar air heater systems.