Browse Articles

Q-CaDD: accelerating in silico methodologies with quantum computation and machine learning for Epidermal growth factor receptor

Accurate helium–benzene potential: From CCSD(T) to Gaussian process regression

The accurate modeling of non-covalent interactions between helium and graphitic materials is important for understanding quantum phenomena in reduced dimensions, with the helium–benzene complex serving as the fundamental prototype. However, creating a quantitatively reliable potential energy surface (PES) for this weakly bound system remains a significant computational challenge. In this study, we present a comprehensive, multi-level investigation of the He–benzene interaction, establishing benchmark energies using high-level coupled-cluster singles-and-doubles with perturbative triples [CCSD(T)] methods extrapolated to the complete basis set limit and assessing higher-order contributions. We use symmetry-adapted perturbation theory to benchmark it against CCSD(T) and to decompose the interaction into its physical components—confirming it is dominated by a balance between dispersion and exchange-repulsion. A continuous, three-dimensional PES is constructed from discrete ab initio points using multifidelity Gaussian process regression that combines density functional theory results with sparse coupled-cluster energies. The result is a highly accurate PES with sub-cm−1 accuracy that obeys physical laws. This new PES is applied to path integral Monte Carlo (PIMC) simulations to study the solvation of 4He atoms on benzene at low temperatures. Our PIMC results reveal qualitatively different solvation behavior, particularly in the filling of adsorption layers, when compared to simulations using commonly employed empirical Lennard-Jones potentials. This study provides a benchmark PES essential for accurate many-body simulations of helium on larger polycyclic aromatic hydrocarbons toward graphene.

Effect of polyacrylamide concentration on runoff redistribution and sediment yield control from karst spoil heaps under simulated rainfall

Homochiral to heterochiral transition in pentahelicene monolayer on the Pb(111) surface

The chiral self-assembly of racemic pentahelicene molecules on the Pb(111) surface has been systematically investigated using low-temperature scanning tunneling microscopy combined with density functional theory calculations. At low-temperature (∼100 K), molecular diffusion is strongly suppressed, yielding only isolated molecules and small clusters. In contrast, room-temperature deposition enables a well-defined coverage-dependent phase evolution: from a disordered gaseous phase to a homochiral honeycomb lattice, followed by a chiral-alternated checkerboard superlattice, and ultimately to an ordered racemic phase composed of periodically alternating upright heterochiral (M–P) dimers at monolayer coverage. The transition is cooperatively driven by molecular reorientation (flat → upright) and chiral modulation (homochiral → heterochiral), demonstrating a dynamic and tip-mediated reversible chiral segregation process. These findings offer fundamental insights into two-dimensional chiral crystallization and pave the way for designing chiral functional materials with tailored superstructures.

A crisscross-strategy-boosted beaver behavior optimizer for global optimization and oil reservoir production

Mathematical inversion of covariance-map images to determine three-dimensional product scattering distributions

Two-dimensional “crushed” velocity-map images of photoinduced or electron-induced reaction products can be Abel-inverted to reconstruct the full three-dimensional scattering distribution. Here, we develop the equivalent of the inverse Abel transform for covariance-map images, which are increasingly used in the field of velocity-map imaging to uncover correlations between the velocities of two or more scattered products. To formulate the inverse problem, we first derive the forward transformation, which maps the underlying cylindrically symmetric scattering distribution to its corresponding covariance-map measurement. We express the unknown scattering distribution as an expansion in an orthonormal basis and reformulate the inverse problem as a linear least-squares problem, which we solve via QR decomposition. This approach provides flexible control over the reconstruction resolution and supports natural regularization through both basis truncation and data smoothing. We validate the method on synthetic test problems, demonstrating accurate recovery of both local and extended structures, and show convergence using error metrics. We then apply our method to the experimental data on the dissociation products of CF3I2+ generated in electron–molecule collisions. For a simple binary fragmentation channel, the method recovers a localized point-source distribution that is consistent with physical understanding. For a more complex channel, we show that appropriate regularization obtains stable, interpretable results. Our results demonstrate that this method is robust, flexible, and well-suited for analyzing high-quality covariance-map scattering data.

Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning

QMCkl: A kernel library for quantum Monte Carlo applications

Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provides a modular, portable collection of high-performance kernels implementing the core building blocks of QMC calculations. It offers a C-compatible application programming interface, supports the TREXIO standard for input, and covers essential QMC kernels including atomic and molecular orbitals, cusp corrections, the Jastrow factor, and the necessary derivatives also to perform variational and structural optimization. QMCkl separates algorithmic development from hardware-specific tuning by combining human-readable reference implementations with performance-optimized kernels that produce identical numerical results. The library enables consistent, efficient, and reproducible simulations across different QMC codes and architectures and achieves substantial speedups in the evaluation of the energy and its derivatives. Beyond QMC, QMCkl can accelerate deterministic quantum chemistry workflows and visualization tools, promoting cross-code interoperability and simplifying high-performance scientific software development.

Histopathologic responses of the dental pulp to an experimental calcium aluminate–calcium silicate based capping material in comparison to mineral trioxide aggregate

Abstract The aim of this study was to compare the effect of Calcium Aluminate- Calcium Silicate based experimental pulp capping material with the commercially available Mineral Trioxide Aggregate (MTA) in terms of dentin bridge formation and pulp response. Four healthy male dogs were selected for this study. Teeth were randomly divided into four main groups (n = 14). Group I: experimental Calcium aluminate pulp capping material was used for pulp capping, Group II: Mineral Trioxide Aggregate pulp capping material was used for pulp capping, Group III: positive control group; no pulp capping material was applied and Group IV: negative control group; intact teeth were selected . Inflammatory response and dentin bridge formation was assessed at 1 month and 3 months. Regarding the inflammatory response; after 1 month, there was a statistically significant difference among all groups with P value < 0.001. Where MTA group showed a statistically significant lower inflammatory scores than the experimental Calcium aluminate group. After 3 months, there was a statistically significant difference among all groups with P value < 0.001. Where there was no statistically significant difference in the inflammatory scores between MTA and the experimental Calcium aluminate groups. For dentin bridge formation; at 3 months, there was a statistically significant difference among all groups at p value < 0.001. Where there was no statistically significant difference between MTA and the experimental Calcium aluminate groups. The new experimental Calcium aluminate-based biomaterial demonstrated good reparative capabilities and achieved the status of a potential material for use in vital pulp treatment.

Polyampholyte model of ion clusters: Double-layer interactions in the presence of dissociated simple salt

We explore interactions between equally charged surfaces in the presence of simple salt and either neutral or monovalently charged polyampholytes. We consider the possibility of using these charged polymers as crude models of ion clusters. The latter have been hypothesized to form in concentrated aqueous salt solutions and are possibly related to anomalous underscreening. This phenomenon usually manifests itself in unexpectedly strong and long-ranged effective forces at very high ionic strengths. If ion clusters are formed, they are expected to carry at most a weak net charge. Keeping this in mind, we investigate how polyampholyte chains mediate interactions between charged surfaces. A significant amount of simple salt is also present in most cases. We highlight that if the charges of the polyampholytes are unevenly distributed, there is a polarization response that, in turn, can generate very strong and long-ranged surface forces, even at rather high concentrations of simple salt. Aside from their possible relevance to ion clusters and underscreening phenomena, these results also suggest the possibility of tailoring synthetic polyampholytes in order to regulate colloidal stability.

A novel bioenergetic model outlines the metabolism of a deep-sea clam and that of its sulfur-oxidizing symbionts

Polariton-mediated control over chemical reactivity in a cavity/non-cavity hybrid system

Vibrational strong coupling (VSC) offers a promising route for modifying ground-state chemical reactivity without altering molecular composition or structure. Yet, the controllability of cavity-mediated kinetic modulation remains limited, and existing characterization methods are insufficient for providing direct measurements under localized VSC conditions. Here, we develop a spatially resolved in situ FT-IR methodology using hybrid cavity/non-cavity windows to directly map reaction kinetics across the cavity boundary using the previously reported deprotection reaction of 1-phenyl-2-trimethylsilylacetylene. This approach enables simultaneous spectroscopic monitoring and kinetic extraction at defined spatial coordinates with micrometer-level precision. We observe a continuous transition of rate constants from suppressed values near the cavity region to non-cavity behavior farther away, demonstrating that the VSC-induced reactivity modulation extends beyond the physical cavity boundary via diffusion mechanism. By introducing a controlled gradient in cavity length, we reveal a two-dimensional spatial inhomogeneity of reaction rate constants correlated with both cavity detuning and distance to the cavity boundary. These results show that even submicron variations in cavity thickness can significantly impact kinetic reproducibility, underscoring the importance of spatially resolved real-time measurements for disentangling intrinsic VSC effects from cavity heterogeneity. Our findings establish a robust framework for quantifying spatially localized polaritonic reactivity and highlight the need to carefully control cavity uniformity in future studies of polariton chemistry.

Construction of a diagnostic model for preeclampsia based on differentially expressed lactylation-related genes and the immune infiltration analysis

Six-dimensional state-to-state quantum differential cross sections for the F + CH4 → HF + CH3 reaction

Using the time-dependent wave packet method based on the multiple-step reactant–product decoupling scheme in conjunction with a free-torsion six-dimensional model, we calculate the state-to-state quantum differential cross sections for the F + CH4 → HF + CH3 reaction on a highly accurate neural network PES at collision energies ranging from threshold up to 0.1 eV. The product vibrational and rotational state distributions, angular distributions, and energy partitioning information are analyzed, yielding quantitative agreement with available experimental results. The influences of reaction resonances and collision energy are also discussed in detail.

Hybrid tuned deep learning model for breast cancer diagnosis using genetic data

Abstract The early diagnosis and prognosis of breast cancer is essential for improving breast cancer survival rates and improving breast cancer clinical outcomes. This study aims to provide breast cancer predictive capabilities through the development and application of a robust hybrid computational prediction methodology that performs testing across multiple whole-genome studies; this research was validated using both TCGA (The Cancer Genome Atlas) and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Instead of using traditional methods, where researchers select specific gene sets from the literature, we chose to operate on the highest dimensional input (17,814 genes in TCGA) and the most extensive set of clinical and genomic variables available (503 clinical/genomic features in METABRIC). A multi-stage feature selection process utilizing Random Forest (RF) rankings in conjunction with Association Rule Mining (ARM) was developed to discover important biomarkers. Predictive analysis was performed using a hybrid deep learning model, which contains Convolutional Neural Networks (CNN) in combination with Bidirectional Long Short-Term Memory (BiLSTM) networks, with iterative optimization through the utilization of Bayesian methods. SMOTE and Gaussian noise augmentations were incorporated into the new model to provide additional robustness by addressing class imbalance and minimizing the risk of overfitting (due to the amount of noise present in the training data). The new model outperformed the TCGA-derived model with an accuracy of 97.4% (AUC=0.995), and after validation on the METABRIC dataset, exhibited an even greater accuracy of 99.30% with a 100% recall rate for predicting cancer-related mortality. Through these findings, we have shown that the integration of association-based feature selection with hybrid deep learning architectures has created a tool for breast cancer diagnosis and prognosis that can provide reliable and generalizable results for diverse groups of patients.

Frozen natural orbitals for projection-based embedding method and its application to quantum computation

Projection-based embedding offers a robust framework for modeling chemical processes in large molecular systems. It partitions computations into low-level treatments for the environmental subsystems and high-level treatments for the active subsystems. To address the computational bottleneck of applying high-accuracy wavefunction theory to the active regions, we introduce frozen natural orbitals (FNOs) approach. This technique efficiently compresses the virtual orbital space to generate a compact set of natural orbitals, which significantly accelerates the convergence of correlation energy recovery. We evaluated the FNO-based embedding approach in comparison with two alternative virtual space truncation approaches for a diverse range of molecular systems. Our results demonstrate that the FNO-embedded method exhibits superior performance by delivering accurate correlation energies with a substantially fewer number of virtual orbitals. Its potential for quantum computing is also discussed, as this reduction in orbitals can be directly translated to lower qubit requirements and thereby facilitate the quantum simulations on near-term quantum hardware.

Perception of AI-generated smile versus real orthodontic treatment outcomes among dentists, students, and laypeople

Abstract In orthodontics, the increasing use of AI-generated smile images in patient communication raises ethical and practical concerns about user perception and misinterpretation of these visuals. This cross-sectional, non-probabilistic sample study evaluated the ability of dentists, dental students, and laypeople to distinguish between real and AI-generated orthodontic smile images, and their perceived attractiveness. The final sample consisted of 288 participants, (63.4% female; mean age = 32.4 years) including 76 dentists, 63 dental students, and 149 laypeople. Each participant was presented with three clinical scenarios, mild dental misalignment, midline diastema, and moderate anterior crowding, and viewed randomized sets of images depicting pre-treatment, real post-treatment, and AI-generated smiles. For each image, participants indicated whether they believed it was AI-generated or real and rated its aesthetic appeal using a visual analog scale ranging from 0 to 100. Data were analyzed using descriptive statistics and diagnostic performance metrics (accuracy, sensitivity, specificity, PPV, and NPV), with attractiveness ratings compared between AI-generated and real images. Sensitivity for identifying AI-generated images was low across all groups (< 50%), while specificity for recognizing real images was high (> 87%). Dental students achieved the highest overall accuracy (72.7%), followed by laypeople (66.3%) and dentists (62.6%). AI-generated smiles were consistently rated as significantly more attractive than real outcomes by all groups (mean VAS: 78.8 vs. 37.9; p  < 0.001). AI-generated smile images were less accurately identified and more aesthetically pleasing than real clinical outcomes compared to real post-treatment outcomes, which were more consistently recognized across all participant groups, regardless of treatment type.

Competition between predissociation and autoionization in molecular hydrogen above the ionization threshold: A high-resolution spectroscopy and velocity map imaging study

The dynamics of molecular hydrogen above its ionization threshold represent a fundamental prototype for understanding the competition between predissociation and autoionization in superexcited states. We investigate this competition in the 125 600–126 000 cm−1 region using a combined high-resolution extreme ultraviolet pump–ultraviolet (UV) probe spectroscopy and velocity map imaging technique. The core of our methodology lies in the unambiguous distinction between decay channels by comparing product signals with and without the UV probe laser. This enables the first direct, quantitative measurement of state-specific relative branching ratios among predissociation, autoionization, and (1 + 1′) resonance-enhanced multiphoton ionization. Our key findings reveal that predissociation is the dominant decay channel for most of the studied resonances, implying a faster rate compared to autoionization. Furthermore, the high anisotropy parameter (β ≈ +2) extracted from the fragment angular distributions unambiguously confirms that the effective dissociation proceeds via a parallel transition, with the dissociating state having predominant 1Σu+ symmetry. These results provide a definitive, multidimensional experimental dataset that quantifies the competition between quantum decay pathways in a prototype molecular system.

SHP2 improves ovarian morphology and steroidogenic function in a rat PCOS model by modulating IRE1α/XBP1/NLRP3-mediated granulosa cell pyroptosis

Multidimensional tunneling and reaction-path dynamics in oxygen-insertion reactions of Criegee intermediates with small alkanes

The alcohol-forming reactions of formaldehyde oxide (CH2OO) and its halogenated analogues (CCl2OO and CF2OO) with methane and ethane were investigated using dual-level variational transition state theory with multidimensional tunneling treatments. High-level coupled-cluster single, double, triple/complete basis set energetics were incorporated along minimum-energy paths computed at the MP2/aug-cc-pVDZ level to provide a consistent description of barrier profiles and reaction exothermicities. Representative systems were further analyzed using microcanonical multidimensional tunneling, while small-curvature tunneling treatments were applied to the remaining reactions. The results reveal that the reactions proceed through an asynchronous oxygen-insertion transition region in which hydrogen transfer and O–O bond weakening occur simultaneously, leading to narrow effective barrier regions and pronounced tunneling sensitivity. For CH2OO + CH4, tunneling enhances the canonical rate constant by nearly two orders of magnitude at 200 K, and deuterium substitution yields kinetic isotope effects exceeding one order of magnitude at low temperature. Oxygen-isotope effects approaching two at 200 K further indicate significant heavy-atom tunneling contributions. These reactions provide well-defined theoretical benchmark systems for evaluating tunneling and reaction-path effects in which hydrogen and oxygen tunneling jointly influence rate behavior. Halogen substitution substantially lowers the insertion barrier by up to 16 kcal/mol and produces rate constants approaching those of benchmark hydrogen-abstraction reactions, suggesting that faster halogenated systems may offer more accessible conditions for future experimental validation. These findings highlight the importance of reaction-path curvature and multidimensional tunneling effects in oxygen-insertion reactions and demonstrate that multidimensional tunneling treatments are essential for reliable kinetic predictions.