Browse Articles

Explicitly correlated Gaussian basis approach to periodic systems

Closed-form expressions for all matrix elements required for variational calculation of the electronic structure of periodic solids have been derived using a basis of explicitly correlated Gaussians. Periodic basis functions are constructed by summing shifted correlated Gaussians over all composite lattice translations, where a generalized unfolding theorem reduces the resulting double lattice sum to a single sum through a unified computational framework for overlap, kinetic energy, and Coulomb potential operators. The formalism has been validated through application to an infinite one-dimensional hydrogen chain, where the ground-state energy per atom computed in the thermodynamic limit is shown to agree with finite-chain results extrapolated by many other many-body methods.

Copula-based generation of synthetic multivariate time-series for underground mine gas concentrations

Sensitivity analysis of cycle flux response in nonequilibrium dynamics

The decomposition of edge current into cycle fluxes sheds light on understanding the nonequilibrium steady state structure of stochastic graph models, providing important insights into irreversibility, stability, and dominant functionality of a plethora of nonequilibrium physics. However, the response of the cycle flux to time-dependent perturbations is less well understood. Here, we introduce a theoretical method to analyze the response properties of the cycle flux around the nonequilibrium steady state, rather than the near equilibrium response provided by the well-known linear response theory. We find that both the state and cycle flux responses are determined by the system relaxation spectrum, the signal frequency, as well as the overlap between the signal matrix and the transient states. We further study the relation between response sensitivity and the information gain contained in the stochastic trajectory, providing an information theoretic bound on cycle flux response precision. Furthermore, we use both a quantum Maxwell demon model and a classical chemical reaction network model to illustrate our theory. Our work paves the way toward fully understanding and controlling the dynamical response of complex nonequilibrium stochastic graph models and inferring the hidden time-dependent signals by measuring the observable cycle flux response.

Antimalarial cytoskeletal targeting with broad apicomplexan activity

Malaria is a devastating disease that resulted in an estimated 610,000 deaths in 2024, the majority being children under the age of five. Here, we use KNX-115 to illustrate multistage antiparasitic activity upon targeting the cytoskeletal enzyme Plasmodium falciparum myosin A (PfMyoA). KNX-115 inhibits purified actin-activated ATPase with a potency in the low nanomolar range and >50-fold selectivity against cardiac, skeletal, and smooth muscle myosins. KNX-115 traps PfMyoA in a state that binds weakly to actin. A 2.35 Å resolution structure of KNX-115 bound to PfMyoA reveals critical interactions contributing to its mechanism of action. Importantly, in vitro evolution data reveal that KNX-115 engages PfMyoA as a sole cellular target. Inhibiting PfMyoA blocks the development of the blood and liver stages of laboratory strains of P. falciparum , with no liver cell toxicity, sporozoite cell traversal and motility, and sporozoite development in the mosquito. Inhibiting PfMyoA completely kills parasites after 96 h of treatment. Furthermore, KNX-115 is equally effective at inhibiting a panel of Plasmodium strains resistant to experimental and marketed antimalarials and shows inhibitory activity against P. falciparum circulating isolates from the Brazilian Amazon. Inhibiting PfMyoA with KNX-115 also blocks the blood stage of a laboratory strain of Plasmodium vivax . In line with the evolutionary identity of MyoA among various apicomplexan parasites, KNX-115 also inhibits Cryptosporidium and Eimeria MyoA in vitro and is an effective inhibitor of Cryptosporidium , Toxoplasma, and Eimeria cellular growth, with EC 50 s similar to those found for blood and liver stage Plasmodium .

Ultrasound assessment of the diaphragm and abdominal muscles among elite wrestlers with and without low back pain- a case-control study

Quantifying the relationship between electric field enhancement and plasmon-driven electron transfer

Plasmonic materials interact strongly with light to create localized, out-of-equilibrium environments with intense electromagnetic fields known as hotspots. After forming, hotspots dissipate energy into their surroundings and can transfer energy and charge carriers to nearby molecules, giving plasmonic materials the potential to drive reactions with sunlight. However, the field needs a better mechanistic understanding of plasmon–molecule interactions and how the local plasmon environment, specifically the electromagnetic field enhancement and spatial distribution of hotspots, impacts the reaction yield. In this work, we mapped plasmon-driven charge transfer across ordered plasmonic substrates using diffraction-limited surface-enhanced Raman spectroscopy (SERS) microscopy to understand the relationship between the average local electric field enhancement and charge transfer reaction yield. We tracked the plasmon-induced electron transfer to buckminsterfullerene (C60) and found that areas with the greatest SERS intensity were not the areas with the greatest ensemble-averaged reduction of C60, suggesting that areas with higher electric field enhancement—or “hotter,” more enhancing hotspots—do not improve the charge transfer reaction yield. This work shows that efforts to improve plasmon-driven charge transfer should not merely focus on creating substrates with extremely enhancing regions but also consider how other factors could optimize photoreduction yields.

Resolving weak moments in canted antiferromagnets via time-domain terahertz emission spectroscopy

Canted antiferromagnets host weak net magnetization arising from spin canting while preserving the ultrafast response of antiferromagnetic order. Probing such minute moments—especially in thin films—remains challenging, as conventional magneto-optical and electrical techniques lack sufficient sensitivity. Here, we employ time-domain terahertz emission spectroscopy as a contact-free probe of such weak magnetism. Using epitaxial TmFeO 3 /Pt heterostructures as a model system, the emitted THz signal directly follows the orientation of the canted Fe 3+ moment, allowing real-time tracking of its continuous rotation through the spin reorientation transition. Moreover, the THz hysteresis polarity is reversed compared with conventional ferromagnet/Pt systems, indicating an unconventional interfacial magnetic configuration. Our findings establish THz emission spectroscopy as a powerful tool for resolving ultrafast THz emission response in canted antiferromagnetic thin and ultrathin films.

Cross-validation and cross-cultural invariance of the 10-item athletic mental energy scale

<i>In</i> <i>silico</i> biophysical screening of surface ligands for enhanced nanoparticle mucopermeation

The mucus layer, a physiologically relevant hydrogel found in the human gut, respiratory and genital tracts, and on ocular surfaces, forms a formidable physiological barrier with mucin proteins, creating its hygroscopic mesh that restricts the permeation of nanomedicines. Hydrophilic ligands such as polyethylene glycol, when grafted onto nanoparticles, are known to enhance mucopermeation, although the mechanisms remain unclear. To investigate this, 79 ligands were virtually selected from the ChEMBL and DrugBank databases based on their similarity to triethylene glycol (TEG), an analog of polyethylene glycol, and on Lipinski–Veber oral bioavailability parameters. The ligands were ranked by similarity score (SS), proposed here as a structural bioinformatic parameter for automated ligand-candidate prioritization. A final set of 15 ligands was selected and divided into terciles. The top tercile, which included triethylene glycol, had the highest SS values. These 15 ligands were docked with the equilibrated (unglycosylated) MUC5AC protein structure using the PatchDock and AutoDock Vina programs. For each ligand, the average docking or binding score (depending on the program used) and the average atomic contact energy (mean of the top five atomic contact energies) were calculated. The SS values correlated strongly with the average atomic contact energies and moderately with the average docking and binding scores, as well as with the Lipinski–Veber parameters. These trends were confirmed using 15 additional ligands docked on unglycosylated mucin, with the two top- and two bottom-ranked ligands and TEG further evaluated on glycosylated MUC5AC using PatchDock. Overall, the SS and the average atomic contact energy emerged as effective indicators for efficient and accurate surface-ligand screening.

A unified machine-learning framework for ab initio multiscale modeling of liquids

Understanding and predicting the behavior of liquid matter across length scales—using only the microscopic interactions encoded in the Schrödinger equation—remains a central challenge in the physical sciences. Achieving this goal requires not only an accurate and efficient description of intermolecular forces but also a consistent framework that bridges the micro-, meso-, and macroscales. Here, by combining machine-learned interatomic potentials (MLIPs) with neural classical density functional theory (cDFT), we present such a framework. MLIPs trained on quantum-mechanical energies and forces are used to generate inhomogeneous density profiles, which then serve as the training data for neural cDFT. The resulting ab initio neural cDFT is more computationally efficient than molecular simulations and provides a conceptually transparent route to the thermodynamics of both homogeneous and planar inhomogeneous systems. We demonstrate the approach for both water and carbon dioxide using several exchange–correlation functionals. Beyond accurately reproducing—at the level of the underlying approximate electronic structure—bulk equations of state and liquid–vapor phase diagrams, ab initio neural cDFT predicts, from first principles, how confinement modifies liquid–vapor coexistence in water. It also captures complex behavior in supercritical carbon dioxide such as the Fisher–Widom and Widom lines. While current applications are limited to bulk fluids and planar geometries, this approach establishes a general first-principles route to multiscale modeling of fluids by unifying two independently developed machine-learning paradigms. This work represents an important step toward generalizing cDFT beyond simple empirical potentials to chemically complex systems.

An intelligent synapse fusion network with dynamic adaptive control for multimodal fusion

Electronic structure and metal–metal bonding in early lanthanide dimers La2, Ce2, and Pr2

Understanding whether 4f electrons participate directly in lanthanide–lanthanide bonding remains a long-standing question in f-element chemistry. Lanthanide dimers provide the simplest molecular platform for isolating intrinsic metal–metal bonding interactions without ligand-field or oxidation-state complications. Here, we present a fully relativistic multireference investigation of La2, Ce2, and Pr2 to resolve their ground electronic states and elucidate the evolution of bonding across the early lanthanide series. The ground states under spin–orbit coupling are determined to be 0g, 1g, and 2u for La2, Ce2, and Pr2, respectively, with spectroscopic constants in improved agreement with available experimental data. Molecular orbital analysis reveals a common σ2π4 inner-core framework, while Ce2 exhibits the highest bond order, consistent with partial multiple-bonding character. Importantly, the results demonstrate that 4f orbitals contribute to metal–metal bonding only through cooperative interaction with energetically accessible 5d orbitals; in the absence of such mixing, the 4f electrons remain essentially nonbonding. These findings provide a unified chemical picture for the onset of f-electron participation in lanthanide bonding and clarify the electronic origin of metal–metal interactions in early lanthanide dimers.

Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution

The rise of multimodal generative AI transforms the intersection of technology and art, offering richer insights into large-scale artworks. While significant research has focused on their creative potential, their ability to represent artworks in latent spaces remains underexamined. We use generative AI, specifically Stable Diffusion, to analyze 500 y of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subjects). Our findings reveal that contextual information exhibits stronger vector alignment and orientation with conventional artistic periods, styles, and individual artists than formal elements. Also, we show how artistic expression aligns with historical shifts using contextual keywords extracted from paintings. Our generative experiment, infusing prospective contexts into historical artworks, validates this vector alignment and orientation by synthesizing artworks consistent with the stylistic patterns of target periods. This study demonstrates how multimodal AI expands traditional formal analysis by integrating temporal, cultural, and historical contexts to quantify the latent structure of cultural knowledge.

LCA analysis of conventional and organic onion production in Poland

Abstract This study presents a comparative analysis of onion production technologies under conventional and organic farming systems, focusing on their environmental impacts. The research covered onion cultivation in southern Poland—a region that is not only one of the country’s leading production areas, but also among the largest in Europe. A “cradle-to-gate” approach was applied, since at this stage of production considerable technological differences exist between the two cultivation systems. The analysis considered the type of field operations, the machines used and their operating time, the amounts of fertilizers, pesticides, fuel, and water consumed. Onion yield was measured, and the results were related both to cultivation area (1 ha) and production volume (1 Mg). To identify the environmental relationships of all inputs and outputs within the LCA scope and to estimate their impacts, the software package SimaPro (version 8.1.0.60) was applied. The results demonstrated greater negative environmental impact of individual operations in conventional onion cultivation compared to organic farming, regardless of the chosen functional unit. Critical processes were identified in terms of environmental burden: harvesting in both systems, and mineral fertilization in the case of conventional cultivation.

VAMP7-dependent mitochondria–lysosome contacts contribute to glial mitochondrial dynamics and dopaminergic neuron survival

Although disrupted mitochondrial dynamics in neurons are closely linked to neurodegenerative diseases, far less is known about how mitochondrial dynamics are regulated in glia or whether glial mitochondrial dysfunction contributes to neurodegeneration. Here, we show that the R-SNARE protein VAMP7 regulates the untethering of mitochondria–lysosome contacts (MLCs) in adult fly glia. Glial-specific knockdown of VAMP7 leads to prolonged MLCs and mitochondrial elongation associated with altered fission/fusion dynamics. These VAMP7-deficient mitochondria exhibit hyperpolarized membrane potential, leading to increased reactive oxygen species production, lipid droplet accumulation, and dopaminergic neurodegeneration. Mechanistically, VAMP7 interacts with the GTPase-activating protein TBC1D15-17 to promote Rab7 GTP hydrolysis. Without VAMP7, TBC1D15-17 remains bound to Rab7 but fails to activate its hydrolysis, resulting in elevated GTP-bound Rab7 and impaired MLCs untethering. Consistently, expression of GTP-locked Rab7 Q67L or GTPase-activating protein-dead TBC1D15-17 ΔGAP phenocopies the mitochondrial defects, while GDP-bound Rab7 T22N or wild-type TBC1D15-17 restores the MLC dynamics. Considering that SNARE proteins mediate membrane fusion, our results demonstrate a role for VAMP7 in glial mitochondrial dynamics via organelle contacts, impacting neuron survival in a non-cell-autonomous manner.

Potential of cross-task stylus-based drawing features for predicting psychological vulnerability in children

Glutamate administration is associated with aggravated atherosclerosis and altered expression of RAPGEF1, OPN, and MYL7 in High-Fat Diet-Fed ApoE⁻/⁻ Mice

Objective This study aims to examine whether oral glutamate administration is associated with accelerated atherosclerosis progression in high-fat diet-fed ApoE ⁻ / ⁻ mice and to identify candidate protein associated with this process. Methods Atherosclerosis (AS) was induced in ApoE ⁻ / ⁻ mice by feeding a high-fat diet (HFD) for three months. The mice were randomly divided into four groups: (1) the AS-GLU group, receiving glutamate with HFD; (2) the AS group, receiving HFD with placebo; (3) the Glu group, receiving glutamate with normal chow; and (4) the Control group, receiving normal chow with a placebo. Atherosclerotic lesions were assessed using Lillie's oil red O staining and histological analysis. LC-MS/MS based proteomics profiling was employed to identify differentially expressed proteins, with subsequent validation by Western blotting and qPCR. Results Glutamate administration alone (Glu group) induced minimal plaque formation. The combination of HFD and glutamate (AS-GLU) was associated with a greater atherosclerotic plaque area compared to HFD alone (AS group). Proteomics identified RAPGEF1, OPN, and MYL7 as proteins whose expression was increased in the AS-GLU group compared to AS group. These associations were confirmed by Western blot and qPCR. Conclusions This study shows that oral glutamate administration, in the context of a high-fat diet in ApoE ⁻ / ⁻ mice, is associated with exacerbated atherosclerosis and increased expression of RAPGEF1, OPN, and MYL7. These proteins represent candidate molecules associated with glutamate exposure, but their causal role remains to be determined. Serum glutamate levels were not significantly elevated, and lipid profile changes may also contribute to the observed associations.

Thermodynamic and mechanical evaluation of moisture susceptibility in asphalt mixtures modified with aminated recycled PVC

Abstract The escalating accumulation of plastic waste, particularly polyvinyl chloride (PVC), necessitates sustainable recycling strategies in infrastructure engineering. This study investigates the chemical transformation of waste PVC into aminated recycled PVC (APVC) as a high-performance bitumen modifier to enhance the moisture stripping resistance of asphalt mixtures. APVC was synthesized through chemical amination and verified using FTIR. The research employed a multi-scale approach, integrating surface free energy (SFE) theory to characterize bitumen-water interactions with macro-scale mechanical testing, supported by aggregate surface analysis via the universal sorption device (USD). Results indicated that APVC modification significantly increased the total SFE of the bitumen, specifically boosting the basic component. This is attributed to the introduction of amine functional groups acting as Lewis bases, which establish strong chemical anchoring with the acidic silanol sites of granite. Thermodynamic analysis revealed that APVC was exceptionally effective in reducing debonding energy, with the granite-bitumen system surpassing the limestone system in thermodynamic resistance to debonding at a 6% dosage. Macroscopic evaluations via indirect tensile strength (ITS) tests confirmed these findings, with statistical validation via Tukey’s HSD test ( P  &lt; 0.05). The TSR of granite mixtures improved from 73.3% to 88.7%. A clear correlation was established between thermodynamic indices and mechanical performance. Ultimately, APVC serves as a potent anti-stripping agent that can potentially extend the service life of pavements by mitigating moisture-induced distresses like potholes in real-world conditions.

The social-class gap in negotiation: Lower-class individuals negotiate less and face more backlash

Economic inequality has widened considerably in the United States and globally, raising pressing questions about the mechanisms that reproduce social stratification. One prominent manifestation of this inequality is the “class ceiling” in organizations: individuals from lower social classes earn less and advance more slowly than their higher-class counterparts, even with comparable credentials. We identify a crucial behavioral pathway contributing to this class ceiling: a social-class gap in the propensity to negotiate. We propose that lower-class individuals are less likely to initiate negotiations than their higher-class counterparts, a pattern that can compound economic disadvantage over time. Across four studies (total N = 10,211)—including a nationally representative sample of employees (Study 1), a sample of business school students (Study 2), a field study of an online labor market (Study 3), and a survey study of workplace negotiations and career outcomes (Study 4)—we find consistent evidence for this gap. This gap is partially explained by lower-class individuals’ reduced sense of power and heightened concerns about social backlash. Additionally, Study 5 ( N = 1,133) finds that such concerns may be justified: an experiment with human resources (HR) professionals shows that identical negotiation requests elicit stronger social backlash when initiated by lower-class (vs. higher-class) individuals. Together, these findings reveal a pernicious double bind for lower-class individuals: remaining silent perpetuates economic disadvantage, yet initiating negotiation exposes them to greater social penalties. Addressing this double bind is critical for ensuring that negotiation serves as a vehicle for upward mobility rather than a gatekeeper of privilege.

Contemporary patients with atrial fibrillation are not anticoagulated despite risks of stroke - Insights from GARDENIA

Background GARDENIA is a global, multicentre, prospective, non-interventional study in high-risk atrial fibrillation (AF) patients, who were untreated with anticoagulants. Objectives Provide contemporary evidence on reasons for non-treatment with an anticoagulant, and outcomes in high-risk AF patients. Methods GARDENIA enrolled 704 AF patients with a CHA 2 DS 2 -VA score ≥2 and an elevated risk of bleeding (older age (≥70 years), reduced renal function, concomitant antiplatelet or NSAID use, or other conditions associated with increased bleeding risk), and had not been recently treated with an oral anticoagulant (OAC). Patients were followed for at least 4 months. The two-year risks of mortality, stroke and major bleeding with or without direct OACs (DOACs) in these patients was estimated using the GARFIELD risk score. Results History of major bleeding (18.7%), physician-assessed high-risk of bleeding (17.1%), and patient refusal (17.6%) were the top reasons for not initiating OACs. The 100-person-year event rates (95% CI) for mortality, stroke/systemic embolism (SE)/transient ischemic attack (TIA), and ISTH major bleeding were 13.22 (10.33, 16.92), 2.12 (1.14, 3.94), and 2.21 (1.13, 3.91), respectively. GARFIELD risk analysis indicated a 31% and 40% increase in the risk of mortality and stroke, respectively, and a ~ 30% decrease in risk of major bleeding, compared to the estimated risk, had the patients been anticoagulated. The study was prematurely terminated due to low rate of patient accrual. Conclusions Perceived risk of bleeding was the leading cause for non-treatment with OACs in high-risk AF patients. Risk prediction modelling indicated that the benefits of anticoagulation in stroke and mortality reduction outweigh the risk of bleeding.