Browse Articles

Integrated biomarkers of oxidative stress NLRP3 inflammasome and kynurenine pathway reflect disease severity and diagnostic discrimination in depression

Interparticle interactions in nonlocal media: Attraction and repulsion from charge-polarization coupling

Recent measurements of microsphere interactions in diverse media suggest that the standard dielectric-continuum models of solution-phase interactions are fundamentally incomplete. Experiments indicate that the interactions of charged particles in liquids can be dominated by solvent structuring at interfaces, thereby motivating the concept of electrosolvation force. While interfacial spectroscopy and molecular simulations have established that solvent molecules can exhibit net orientation at interfaces, conventional theoretical frameworks treat the fluid as a structureless medium described by a constant dielectric permittivity. This view does not envisage a contribution of interfacial polarization to interactions at longer range. Here, we employ nonlocal dielectric theory accounting for spatial correlations in polarization to describe interactions in solution. This model permits both charge and polarization to govern interactions, leading to dramatic departures from classical expectations. Specifically, the balance between charge and polarization generates a framework of symmetric (repulsive) and antisymmetric (attractive) interactions, wherein: (i) like-charged surfaces can attract at long range, (ii) oppositely charged objects can repel, and (iii) neutral matter can acquire effective electrical mobility and display long-range forces—potentially explaining long-range hydrophobic attraction. Furthermore, like-charged biomolecules can attract in aqueous electrolytes even for modest polarization correlation lengths (ξ = 2 Å). Our results also suggest that electrosolvation effects may underpin flocculation in suspended matter, which has traditionally been attributed to attractive dispersion forces. These findings indicate how solvent structuring and correlations may play a dominant, complex role in fluid-phase physics, necessitating a shift beyond traditional continuum models to accurately describe and understand soft matter and biological interactions.

Synthesis, structural features, and adsorption performance of ZIF-8@hydroxyapatite hybrid composites for continuous adsorption systems

Abstract A hybrid composite based on a metal–organic framework (MOF), zeolitic imidazolate framework-8 (ZIF–8), and hydroxyapatite (HAp) was successfully synthesized via a straightforward and reproducible approach to address the limitations of MOFs in practical applications. While MOFs are well known for their high surface area and tunable porosity, their limited stability can restrict their use in aqueous systems. The integration of ZIF–8 with hydroxyapatite provides a synergistic platform that enhances structural robustness and surface reactivity while maintaining the intrinsic adsorption properties of the MOF. The resulting ZIF–8@HAp composite was characterized by powder X-ray diffraction (PXRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM), which confirmed both the preservation of the crystalline of the ZIF–8 and its successful integration onto the HAp matrix, while interfacial interaction mechanisms in the ZIF-8@HAp composite were elucidated via NCI, RDG, HS, and fingerprint plot analyses. Brunauer–Emmett–Teller (BET) analysis revealed a high specific surface area along with a well-structured and accessible pores. The adsorption performance of the composite was investigated using malachite green as a model organic dye. To evaluate process efficiency and optimize operational conditions, particular emphasis was placed on continuous fixed-bed column adsorption. The influence of key parameters, namely flow rate and bed height, was systematically examined to determine optimal working conditions. The results clearly indicated that these variables play a critical role in governing adsorption efficiency. Under optimal conditions (Q = 0.1 mL/min and H = 30 cm), a maximum adsorption capacity of 100.08 mg/g was achieved, underscoring the strong potential of this material for wastewater treatment. Overall, these findings demonstrate that ZIF–8@HAp is a promising, efficient, and stable MOF-based adsorbent for environmental remediation applications.

Reweighting estimators for density response in path integral Monte Carlo: Applications to linear, nonlinear, and cross-species density response

We present density response estimators for Monte Carlo simulations that are based on a reweighting procedure, where the samples of an unperturbed multi-component system are used to estimate the properties of a system perturbed by an external harmonic potential. This allows the species-resolved linear and nonlinear static density response to be estimated purely from simulations of the unperturbed system. The method is demonstrated for the uniform electron gas under warm dense matter and strongly coupled conditions using ab initio path integral Monte Carlo simulations. The performance of the method with respect to the number of particles and the number of imaginary time slices is investigated. The scheme is generalized to consider multiple external perturbations, acting on different species and with different wavenumbers, giving one access to additional cross-species density response functions and the complete quadratic response function resolved for both wavenumber arguments through mode coupling. The flexibility of the methodology opens the possibility to investigate numerous new density response properties to further advance our understanding of interacting quantum many-body systems across a broad range of applications.

Cardiovascular mortality associated with colorectal cancer: a population-based observational study using the SEER database

Monte Carlo simulations of NMR high-field <i>T</i> 2 relaxation induced by superparamagnetic iron oxide nanoparticles coated with a layer with slowed water diffusion

In nuclear magnetic resonance imaging, superparamagnetic iron oxide nanoparticles (SPIONs) are an alternative contrast agent to gadolinium, as they can be used as negative contrast agents. They are usually coated with polymers or sugars for stability and biocompatibility. Coatings are at best semipermeable to water. In existing theories of SPION-induced nuclear magnetic resonance contrast, T2 contrast relies on diffusion of water protons in the magnetic field inhomogeneities created by the nanoparticles; therefore, reduced diffusion in the coating is expected to impact the relaxation rate of protons surrounding SPIONs. In this study, the impact on transverse relaxation of a coating layer surrounding SPIONs in which water diffusion is slowed is studied through a Monte Carlo algorithm. It is shown that for small SPIONs, the presence of a coating does affect the transverse relaxation rate R2. The transverse relaxation rate is higher and, therefore, SPION-induced contrast performance is enhanced for nanoparticles with a magnetic iron oxide core smaller than 10 nm in radius, with sufficiently thick coatings with water diffusion coefficients typical of polyethylene glycol, dextran, and gelatin. Those R2 variations can be semi-quantitatively understood using an exchange model, where the protons inside the coating contribute to the relaxation rate with a weight that takes into account their residence time in the coating. Eventually, this study provides guidelines for SPION synthesis for optimum contrast performance: small SPIONs with bigger coatings where the diffusion coefficient of water is 3–10 times lower than the self-diffusion coefficient of water perform the best as T2 contrast agents. Such coatings can be polyethylene glycol or polyacrylic acid, dextran, and gelatin.

Evaluation of claudin-2, claudin-3 and claudin-4 proteins in children with inflammatory bowel diseases

Cooperative stability, many-body expansion, and σ-aromaticity of (LiH) <i>n</i> clusters ( <i>n</i> = 1–6): A CCSD(T) study at the complete basis set limit

We investigated the energetics and bonding of lithium hydride clusters (LiH)n (n = 1–6) using a composite ab initio scheme inspired by W2 theory to achieve sub-kcal/mol accuracy. This approach combines CCSD(T) results extrapolated to the complete basis set limit with a rigorous treatment of core–valence correlation, scalar relativistic effects, and the diagonal Born–Oppenheimer correction. Our results show that while Hartree–Fock theory captures the primary electrostatic binding, correlation effects are crucial for determining the energetic preference of compact isomers over cyclic rings. A parallel density functional theory study shows that while standard hybrid functionals like B3LYP-D4 and M06-2X exhibit larger deviations, the double-hybrid revDSD-PBEP86-D4 functional closely matches our benchmarks, delivering sub-kcal/mol accuracy. Structural and chemical bonding analyses, including intrinsic bond orbital, nucleus-independent chemical shift, and many-body expansion (MBE) methods, reveal high ionic character and multi-center bonding (3c–2e and 4c–2e) within the (LiH)n clusters. MBE analysis of the interaction energy of the monocyclic clusters with respect to the LiH molecules reveals that the two- and three-body terms are consistently negative (stabilizing), while all higher-order terms are negligible. We find that σ-aromaticity in these systems is predominantly local and bond-centered. As the rings expand, the interior becomes magnetically decoupled from the σ-skeleton, precluding the formation of a global ring current. These results establish definitive benchmarks for the stability of prototypical electron-deficient clusters.

Climate velocity and human pressures shape global range contraction in the leopard

Simulating hydrodynamic interactions in colloidal suspensions using multiparticle collision dynamics with rigid-body constraints

We develop a method for simulating colloidal suspensions using multiparticle collision dynamics (MPCD) with a discrete particle model represented as a rigid body. The key steps for incorporating the rigid-body constraints are to thermalize the velocities of the discrete sites before they participate in the MPCD collision step, then transfer momentum from the sites to the rigid body. We demonstrate that the rigid-body model produces the expected statistics for a single spherical particle and the same transport properties for a hard-sphere colloidal suspension as an equivalent model using harmonic bonds to maintain the site geometry. Importantly, the rigid-body model has less computational overhead and may permit a larger simulation time step than the harmonic-bond model, leading to a nearly order of magnitude speedup in benchmark simulations of hard-sphere colloidal suspensions. Our method is compatible with arbitrary discretization, so it enables more efficient MPCD simulations of suspensions of colloidal particles with complex shapes.

A novel 8D hyperchaotic framework for robust and key-dependent substitution box design

Calcium complexation in aqueous solution: An x-ray and <i>ab initio</i> approach

Understanding the aqueous chemistry of cations in solution is central to modeling ion transport, complexation, and reactivity. Calcium, for example, plays a critical role in biological and environmental systems; however, a fundamental understanding of its coordination in dilute, aqueous solution is lacking. There is still debate regarding the number of water molecules in its first solvation shell. Moreover, studies of Ca2+ coordination in aqueous complexes with ligands other than water are rare. Herein, we apply Ca K-edge x-ray absorption near-edge structure (XANES) and extended x-ray absorption fine structure (EXAFS) spectroscopy combined with ab initio molecular dynamics and time-dependent density functional theory to investigate the coordination environment of aqueous Ca2+. On the one hand, EXAFS spectroscopy is sensitive to bond distances; however, the determination of coordination numbers can be imprecise. XANES spectroscopy, on the other hand, is sensitive to molecular symmetry and therefore to coordination number. We first confirmed that calcium is coordinated, on average, by seven water molecules in dilute aqueous solution. We then extended this approach to examine the coordination environment of aqueous Ca2+ ethylenediaminetetraacetic acid (EDTA) complexes. Notably, a water molecule was present in the first coordination shell of Ca2+, in addition to four O atoms and two N atoms from EDTA4−, such that Ca2+ achieved sevenfold coordination. We conclude that Ca2+ tends to adopt low-symmetry, 7-coordinate complexes in aqueous solution, even in the presence of a hexadentate chelator. Our work lays the groundwork needed to understand Ca coordination in numerous biological and environmental systems.

The impact of PM2.5 component exposure during early pregnancy on congenital heart disease: evidence from Guangdong, China

Abstract Limited is known about how the components of particulate matter with aerodynamic diameter &lt; 2.5 μm (PM 2.5 ) are related to congenital heart disease (CHD). We aimed to demonstrate the relationship between exposure to PM 2.5 components during early pregnancy and the risk of CHD. This study included 2,601,300 live births and stillbirths in Guangdong, China from 2014 to 2017. Satellite remote sensing data of PM 2.5 components were collected, including organic matter (OM), sulfate ( $${\text{S}\text{O}}_{4}^{2-}$$ ), nitrate ( $${\text{N}\text{O}}_{3}^{-}$$ ), ammonium ( $${\text{N}\text{H}}_{4}^{+}$$ ), and black carbon (BC). Unconditional logistic regression models were constructed to estimate the relationship of each PM 2.5 component to CHD. The quantile g-computation method was used to evaluate the contribution weight of each PM 2.5 component in relation to CHD in the exposure mixture. The estimated effect size of each PM 2.5 component peaked in the first week of pregnancy. Among them, $${\text{S}\text{O}}_{4}^{2-}$$ exhibited the strongest estimated effect (OR: 2.23, 95% CI 2.19–2.27), followed by BC (OR: 1.95, 95% CI 1.91–1.98). The joint exposure analysis revealed that the estimated weights of $${\text{S}\text{O}}_{4}^{2-}$$ and BC were 86.78% and 13.22%, respectively. These findings suggest that exposure to PM 2.5 components during early pregnancy increases the risk of CHD in the offspring, and $${\text{S}\text{O}}_{4}^{2-}$$ may be the primary PM 2.5 component responsible for this elevated risk.

Functional machine learning modeling of electronic bandgap

We present a systematic study of how functional classification of electronic bandgaps improves subsequent machine learning modelings in a group of more than ten thousand semiconductors and insulators. In this regard, we utilize a homemade Python package, MatFeaLib, for systematic generation of 518 descriptors combining 480 composition-based statistics and 38 structural features, and then apply a hybrid parsimonious feature-selection framework to separate about 20 most important features for our final classification, clustering, and regression tasks. Three application-oriented spectral regions, including infrared, visible, and ultraviolet, are selected for our supervised classification tasks along with seven machine learning classifiers, wherein the Extreme Gradient Boost algorithm achieved the best accuracy of 81%, which increased to 94%, after incorporation of lower-fidelity generalized gradient approximation gaps. A hierarchical approach further improves the accuracy of our functional classification to 85% and 95% in the single- and multi-fidelity schemes, respectively. The SHAP analysis revealed electron number, electronegativity, and average bond length as the most influential descriptors, providing physical insights about the classification process. Class-conditioned bandgap regression exhibits a significant improvement of about 40% relative to a global regression. The relevant error diagnostics indicate that different classes may require distinct modeling approaches to capture the underlying relationships and noise characteristics. Unsupervised clustering with an iterative feature-selection technique is used to identify possible hidden patterns in the dataset, which may improve the subsequent multiclass classification and regression procedures. Our findings may open a new avenue for more accurate materials modeling and, thus, more efficient functional materials discovery.

Single switch high gain DC–DC quadratic boost converter for renewable energy applications

Ethaline deep eutectic solvent under nanoconfinement: Unveiling structural and dynamical changes

Hybrid nanomaterials incorporating deep eutectic solvents (DESs) in porous hosts or at solid interfaces are gaining increasing attention for their potential interest across a wide range of applications. Under these conditions, the performances of DESs may be influenced by interfacial effects and spatial restrictions. In this study, we examined the effects of nanoconfinement on both the structure and molecular dynamics of the prototypical DES ethaline (a mixture of choline chloride and ethylene glycol) when confined within the cylindrical mesopores of SBA-15 (Dp ≈ 8.1 nm) and MCM-41 (Dp ≈ 3.5 nm) silicas, using neutron diffraction and quasielastic neutron scattering. It demonstrates that ethaline remains structurally homogeneous under confinement, showing no evidence of core–shell segregation within the pore cross section. The molecular dynamics of the confined ethaline preserve the key characteristics observed in its bulk state. Translational diffusion follows a jump-diffusion mechanism, with diffusion coefficients that remain remarkably close to the bulk values, showing only a modest reduction in MCM-41. A more pronounced increase in the residence time τ0 between translational molecular jumps is observed. It corresponds to roughly a factor of 3–8 under confinement in SBA-15 and reaches up to a tenfold enhancement in MCM-41 relative to bulk. Similarly, the characteristic relaxation time, τL, associated with the localized in-cage motion of ethaline, increases by ∼20% in SBA-15 and up to 50% in MCM-41. However, the molecular trajectories, modeled from the elastic incoherent structure factor, remain largely preserved under confinement, showing only a marginal reduction in intra-basin motional amplitudes.

CANet: A color-aware convolutional neural network with clinically grounded chromatic learning for skin lesion classification from dermoscopic images

Benchmarking mixed quantum–classical molecular dynamics for electronic strong coupling

Experiments indicate that collective coupling of molecular ensembles to confined optical modes can modify excited-state dynamics and photochemical reactivity. To describe such cavity-induced effects at atomic resolution, semi-classical molecular dynamics approaches have been developed that treat nuclear motion classically while describing the collective light–matter interaction within the Tavis–Cummings framework of quantum electrodynamics. Here, we benchmark mixed quantum–classical approaches, Ehrenfest dynamics, and Fewest-Switches Surface Hopping (FSSH) for simulating nonadiabatic dynamics of electronically strongly coupled carbon monoxide molecules. Their predictions are compared against numerically exact quantum dynamics simulations performed with the multi-configuration time-dependent Hartree method, which treats both electronic and nuclear degrees of freedom quantum mechanically. We find that the semi-classical approaches reproduce the qualitative features of the full quantum dynamics. Quantitative agreement is best achieved with FSSH when a decoherence correction is included. These results demonstrate that mixed quantum–classical methods provide a computationally efficient and quantitatively reliable alternative to fully quantum simulations for investigating nonadiabatic photochemistry under collective electronic strong coupling in systems beyond the reach of exact quantum treatments.

SignatureGuard: hybrid CNN–transformer model for signature verification and identification across Arabic and English datasets

Abstract Offline signature verification has a persistent Latin-script bias: most systems are built and evaluated on English datasets, while Arabic and other non-Latin scripts are largely absent from the benchmarking literature. SignatureGuard is a three-task framework that evaluates six hybrid CNN–transformer architectures on two offline signature benchmarks (one Arabic, ASVAR; one English, CEDAR) under a single shared preprocessing and training pipeline, enabling direct architectural comparison across writing systems. The three tasks are binary forgery detection, multi-class biometric identification, and forgery source identification. To address the Arabic data gap, we publicly released ASVAR: 3471 images (1712 genuine, 1759 forged) from 70 individuals. Hybrid pairings of EfficientNetB7 or ResNet50 with the Vision Transformer (ViT-B/16) achieve test accuracies of 98.2% and 98.4% on forgery detection, macro-F1 above 0.97, and Cohen’s $$\kappa$$ above 0.96; 95% Wilson confidence intervals ( $${\pm }$$ 1.5 pp) confirm these are not artefacts of finite test-set size. MobileNetV2-based hybrids trail by at most 0.7 percentage points. Architectural rankings are broadly consistent across both datasets. All results are obtained under a seen-writer, image-level 80/10/10 split—a closed-set protocol that supports reproducible architectural comparison but overestimates real-world deployment performance; a signer-disjoint evaluation is identified as the primary follow-on experiment. An information-theoretic argument demonstrates that the hybrid classification head cannot perform worse than either frozen component in isolation. Confusion-matrix analysis, multi-seed validation, and backbone fine-tuning are recommended as extensions.

Practical considerations for finite concentration molecular dynamics simulations

Understanding concentrated electrolytes requires a theory that spans local hydration and mesoscale interfacial assembly. We present an integrated workflow—Solvation Characterization via Optimized Probability Ensemble averaging (SCOPE)—that combines (i) enhanced sampling focused on a single Li+ ion, (ii) reweighting of biased trajectories to recover equilibrium microstate probabilities, and (iii) a chemical-potential correction that accounts for the limited reservoir of free water in finite simulation boxes. Applied to LiCl(aq) across 0.5–26M and 283–313 K, this approach reveals a simple organizing principle: solvated ions dominate at low concentrations; contact ion pairs emerge at intermediate strengths; and aggregated Li–xCl clusters become most stable at the solubility limit. The resulting free-energy trends predict temperature-dependent solubility in close agreement with experiment and clarify the role of interfacial nucleation in precipitation. Beyond the simple LiCl(aq) salt considered here, SCOPE offers a transferable strategy for characterizing speciation and phase behavior in concentrated liquid systems where collective coordinates and rare events dominate.