Browse Articles

Early hepatitis B and delta virus kinetics in patients undergoing liver transplantation

Coupled concentration-charge dynamics in 1:1 electrolytes with unequal diffusion coefficients: Local transient response and fluctuations

We investigate the coupled dynamics of concentration and charge in asymmetric 1:1 electrolytes, focusing on the interplay between diffusion asymmetry and external electric fields. Using Brownian dynamics simulations and linearized stochastic density functional theory (SDFT), we analyze the transient response of charge and number currents to inhomogeneous electric fields, as well as the steady-state spatio-temporal fluctuations under uniform fields. Our results reveal that asymmetry in ionic diffusion coefficients introduces a non-trivial coupling between charge and number transport, which modifies the two relaxation modes already present in symmetric electrolytes—a fast one associated with charge relaxation and a slow one linked to ambipolar diffusion. The dynamics are further modulated by the applied field, which enhances diffusion, alters screening lengths, and induces oscillatory behavior in the relaxation modes. The SDFT framework provides closed-form expressions for the intermediate scattering matrix, capturing the dynamics of density fluctuations and cross-correlations between number and charge. These predictions are validated by simulations, demonstrating excellent agreement across a wide range of wave vectors, both at equilibrium and under a finite electric field. Our findings highlight the critical role of diffusion asymmetry and external fields in tuning the transport properties of electrolytes, with implications for nanofluidic devices, energy harvesting, and iontronic circuits. This study bridges theoretical insights with practical applications, offering a robust framework for understanding and controlling electrolyte dynamics in asymmetric systems.

Architectural and geotechnical aspects affecting earthquake resilience for the antique Egyptian Khufu pyramid

Abstract The Great Pyramid of Khufu, completed during Egypt’s Old Kingdom (2600–2450 BCE), exhibits the architectural expertise of ancient Pharaonic Egypt. To understand the structural longevity and earthquake resilience of this iconic monument, we carried out a comprehensive ambient noise survey employing horizontal-to-vertical spectral ratio (HVSR) analysis at 37 measurement points distributed throughout the pyramid’s internal chambers, construction blocks, and adjacent soil. Our analysis reveals several critical findings. First, the pyramid exhibits uniform fundamental frequencies (2.0–2.6 Hz) with an average of ~ 2.3 Hz across all structural elements, indicating exceptional homogeneity in dynamic characteristics. Second, this frequency band differs significantly from that of the surrounding soil (~ 0.6 Hz), preventing resonance amplification through soil-structure interaction—a key mechanism protecting the monument during seismic activity. Third, seismic relative amplification increases systematically with elevation up to 48.68 m, but diminishes substantially within the pressure-relieving chambers (48.86–61.07 m), demonstrating how their geometry actively reduces seismic response. Finally, seismic vulnerability assessment of the subsurface foundation yields a low value (kg = 8.2), confirming excellent bearing capacity and minimal earthquake-induced risk. The low seismic vulnerability index estimated for the foundation soils suggests that any future earthquakes are likely to produce only limited damage to the main pyramid body. These findings present compelling quantitative evidence that ancient Egyptian architects possessed profound geotechnical understanding, optimising structure design and site characterisation to assure millennial-scale stability against seismic hazards.

Transport properties of monodisperse and bidisperse hard-sphere colloidal suspensions from multiparticle collision dynamics simulations

The shear viscosities, long-time self-diffusion coefficients, and sedimentation velocities in monodisperse and bidisperse hard-sphere colloidal suspensions are simulated for volume fractions up to 0.40 using multiparticle collision dynamics with a discrete particle model. The bidisperse suspensions have diameter ratios of 2 and 4 and equal amounts of each particle by volume. All measured properties for monodisperse suspensions are found to be in good agreement with prior literature; however, they highlight the sensitivity of the simulation method to discretization effects. The sedimentation velocities for the bidisperse suspensions are also in reasonable agreement with prior literature, including direction reversal for the smaller particles when the diameter ratio is 4. This work provides reference simulation data for transport properties of colloidal suspensions and establishes the suitability of multiparticle collision dynamics for modeling suspensions of particles with different sizes.

RRM2 as a biomarker and therapeutic target in letrozole resistant estrogen receptor positive breast cancer

Dynamics and charge transport in PVDF-HFP/protic ionic liquid (PIL) membranes: The effect of PIL concentration

The performance of high-temperature proton exchange membranes is critically determined by the interplay between polymer segmental dynamics and ionic transport. In this work, we investigate how the incorporation of a protic ionic liquid (PIL) modifies the molecular dynamics and charge transport behavior of PVDF-HFP-based composite membranes. The glass transition temperature exhibits a pronounced nonmonotonic dependence on PIL content. At low PIL loadings, polymer mobility is unexpectedly reduced, which can be ascribed to strong dipole–dipole interactions between the polymer matrix and the ionic species. Upon further increasing the PIL concentration, these interaction-related constraints are progressively released. This evolution is accompanied by a reduction in crystalline order and enhanced ionic dissociation, giving rise to a substantial increase in proton conductivity. Concomitantly, the dominant relaxation behavior shifts from localized dipolar motions toward cooperative segmental dynamics, reflecting systematic PIL-induced modifications of the polymer environment. In this context, the results clarify how polymer rigidity and ionic mobility are jointly regulated in such composite systems and provide a physically consistent basis for tailoring solid-state proton conductors for operation at elevated temperatures.

Cross-modal semantic scene completion for complex urban traffic scenes with reachability geometric attention

A staging Monte Carlo algorithm for sampling off-diagonal density matrix elements via open-chain path integrals

We introduce a computationally simple algorithm for sampling open-chain distributions within the framework of imaginary-time Feynman path integration. The present method is based on the staging algorithm introduced by Pollock and Ceperley [Phys. Rev. B 30, 2555 (1984)] originally developed for computing position-dependent observables. Here, we sample off-diagonal elements of the density matrix, formulated as a distribution describing a linear polymer-like chain of beads, each connected via nearest-neighbor springs to calculate momentum-dependent quantities. This is achieved using a Monte Carlo scheme that ensures efficient and unbiased sampling of all beads along the chain from the free-particle distribution via a staging transformation; we refer to this approach as staging open path integral Monte Carlo (OPIMC). The proposed algorithm is straightforward to implement, as it only involves sampling Gaussian distributions through a transformation defined by a set of recursion relations, followed by a standard Metropolis acceptance/rejection step. The staging OPIMC method accurately reproduces end-to-end and momentum distributions for quantum systems ranging from coupled harmonic oscillators to liquid water.

Pharmacogenomic landscape of a Moroccan cohort: enhancing global diversity and addressing the North African gap

A computational approach for the calculation of two-dimensional infrared spectra: Application to the amide I band

Two-dimensional (2D) vibrational spectroscopy provides a powerful means to probe molecular interactions, structure, and dynamics in condensed phases with high temporal and spectral resolution. Theoretical and computational studies have played a crucial and complementary role in its advancement through the development of mixed quantum–classical models that connect structural information with the vibrational spectral response. Here, we present an approach for calculating 2D infrared spectra based on the perturbed matrix method. Within this approach, the spectral signal is reconstructed by explicitly calculating the excitonic vibrational states, including the effects of the fluctuating environment without requiring any empirical parameterization. The methodology is validated through the computation of the amide I 2D spectrum of a dipeptide and benchmarked against experimental data. The excellent agreement with the experimental signal and, in particular, the accurate reproduction of the spectral variations experimentally observed upon pH change, allows us to clarify the structural origin of these variations in terms of hydrogen-bonding patterns and the mutual orientation of the side chain and backbone.

Prediction of unerupted canines and premolars widths in an Emirati population: development and validation of regression and machine learning models

Abstract This study aimed to develop a more accurate model for predicting the widths of unerupted canines and premolars in Emirati children, using deep learning and machine learning techniques. Dental models of 380 Emirati individuals aged 15–30 years were collected. The mesiodistal widths of permanent teeth were measured with a standardized orthodontic digital caliper. Regression models were developed using linear regression, Support Vector Regression (SVR; machine learning), and Artificial Neural Networks (ANN; deep learning). The widths of mandibular lateral incisors, central incisors, and the summed width of mandibular incisors were used as predictors. A two-tailed paired t-test was used to assess differences between measured and predicted values. Model performance was evaluated using pass rate (defined as predictions within ± 1 mm of measured values), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ). The dataset was randomly divided into training (70%), validation (20%), and test (10%) sets. A statistically significant difference ( P  < 0.001) was found between the values predicted by the Tanaka–Johnston equations and the measured values. In contrast, no significant differences ( P  > 0.05) were observed between the measured values and those predicted by newly derived models. The highest average pass rate (78.5%, MAE 0.66) was achieved with linear regression using one predictor (summed width of the mandibular incisors). The Tanaka–Johnston method showed limited validity in the Emirati population. Population-specific regression equations significantly improved prediction accuracy, while machine-learning approaches enhanced model stability without outperforming well-calibrated linear regression models, supporting the use of simple, interpretable models for clinically reliable mixed-dentition space analysis.

Excitation of low-frequency modes and the effects of protein dynamics on spectral densities of bacteriochlorophyll molecules

In the theory of open quantum systems, spectral densities are key quantities for modeling the dynamics and spectroscopic properties of the system under investigation. In the case of light-harvesting complexes, they encode the frequency-dependent coupling of electronic excitations in pigment molecules to their environment, reflecting contributions from both intrinsic vibrational modes and the protein surrounding. In particular, the low-frequency components of the spectral densities are crucial for exciton transfer between pigment molecules. Apparently, slow internal modes of bacteriocholophyll molecules in the gas phase are less well represented by common force fields based on classical molecular dynamics simulations. Here, we demonstrate that Born–Oppenheimer molecular dynamics (BOMD) based on the numerically efficient density functional-based tight-binding approach can accurately recover these low-frequency features, whereas normal mode analysis captures them only partially. In contrasting approaches for determining spectral densities, the low-frequency region of the spectral densities obtained is only associated with protein fluctuations; the usage of BOMD, however, also captures the low-frequency contributions arising from slow intramolecular vibrations of the pigment molecules themselves. Notably, this behavior is consistently observed for both the flexible B800 and the more rigid B850 rings in light-harvesting 2 (LH2) complexes of purple bacteria, as well as in the Fenna–Matthews–Olson complex of green sulfur bacteria. Interestingly, we also find that the spectral densities of the pigments in the B850 ring of LH2 are not influenced by the environment, i.e., the gaps between the ground and first excited states are not changed significantly by the fluctuations of the protein environment.

Daily briefing: Wearable robot could help kids with neuromuscular disease stand

A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy

Abstract Accurate quantification of spindle-shaped cells in bright-field microscopy remains challenging due to low contrast, noise, and highly variable cell morphology. Conventional approaches often rely on fluorescent staining or deep learning models, which may introduce phototoxic effects, require extensive training data, or offer limited interpretability. Here, we present a deterministic image analysis method for quantifying spindle-shaped cells directly from noisy bright-field microscopy images without the need for fluorescent labeling or supervised training. The proposed workflow combines contrast enhancement, adaptive thresholding, contour filtering, and shape-guided refinement to detect elongated cell bodies under challenging imaging conditions. The method is designed to be robust to irregular cell morphology and heterogeneous background commonly encountered in bright-field time-lapse microscopy. Quantitative evaluation demonstrates reliable cell counting and consistent performance across varying noise levels and imaging conditions. The approach achieves competitive accuracy while maintaining interpretability and low computational complexity, enabling straightforward integration into existing biomedical imaging workflows. By preserving cell morphology and avoiding fluorescent staining and complex model training, the method provides a practical solution for large-scale analysis of spindle-shaped cell populations in bright-field microscopy experiments.

Inverted perovskite solar cells with N-type organic small molecule dopant optimization

The interface contact properties, charge recombination behavior, and energy level alignment of inverted perovskite solar cells (PSCs) constitute core bottlenecks that restrict their performance improvement and commercialization. Developing electronic transport layer (ETL) materials with superior efficiency and stability represents a crucial technological approach to overcoming these limitations. This study designed and synthesized a novel N-type organic small molecule named SMX2, which was introduced into [6,6]-phenyl-C61-butyric acid methyl ester to construct a composite ETL. Experimental results confirm that SMX2 can improve the interfacial compatibility and contact quality at the ETL–perovskite interface, optimize energy level alignment, passivate defects, and reduce non-radiative recombination. The champion device based on the SMX2-doped ETL achieves a power conversion efficiency of 20.06%. After storage in air for 30 days without encapsulation, the device maintains 92.8% of its initial efficiency. This work provides a valuable theoretical basis and experimental guidance for developing simple and highly effective ETL modification materials, holding significant implications for advancing the development of high-performance and long-lifetime PSCs.

How we’re using AI tools to improve psychedelic-drug research

Prediction of ocular length in hypotony condition: a measurement study using immersion ultrasound biometry and Dino-Lite® microscope in goat eyes

Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network

This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics and to circumvent the computationally expensive time-dependent variational principle required in conventional variational methods. The proposed PINN-DQME method employs time-encoded neural networks within a time-domain decomposition strategy to represent the evolution governed by the dissipaton-embedded quantum master equation (DQME). We implement and validate this approach in the single-impurity Anderson model, benchmarking the PINN-DQME results against the numerically exact hierarchical equations of motion. The PINN-DQME method demonstrates high accuracy in simulating quantum dissipative dynamics at high temperatures, where non-Markovian effects are weak. However, for strongly non-Markovian dynamics at low temperatures, it encounters challenges with error accumulation during time propagation, highlighting an area for future refinement in applying PINNs to complex quantum dynamical settings.

A novel copper-based magnetic nano catalyst for the synthesis of hexahydroquinolines, aminocyanopyridines, and pyranopyrazoles

Evaluating the role of anion structure in the physisorption contribution to CO2 solvation in [BMIm]-based systems: A molecular dynamics study

The urgent need for sustainable carbon capture has established imidazolium-based ionic liquids (ILs) as revolutionary solvents. However, the specific role of physisorption in their capture mechanisms remains largely unexplored. This study uses molecular dynamics simulations and the Bennett acceptance ratio method to analyze the thermodynamic and kinetic properties of CO2 in [BMIm][HCOO], [BMIm][OAc], and [BMIm][C3H5O2] at temperatures between 300 and 400 K. Our findings reveal that [BMIm][HCOO] is the thermodynamic frontrunner, exhibiting the strongest affinity for CO2 with a Henry’s law constant of just 69 bar at 300 K and a substantial physisorption enthalpy of −15.42 kJ/mol. A key finding of this study is that physisorption accounts for around 40% of the total CO2 capture process in acetate and propionate systems, highlighting its significant role in solvation. Furthermore, our data reveal a significant kinetic trade-off: while the formate system demonstrates superior binding strength, [BMIm][OAc] exhibits enhanced diffusion and permeability rates, which are crucial for dynamic membrane applications. We also demonstrate that CO2 solubility is spontaneous below ∼350 K but decreases sharply as temperatures rise, confirming the endothermic nature of the dissolution process. By detailing how anion chain length and Coulombic interactions dictate properties, such as structural flexibility and heat capacity, this study provides a vital blueprint for the rational design of high-efficiency, sustainable ILs for industrial carbon mitigation.