Browse Articles

Human disturbance thresholds determine the ecological role of an apex predator

Abinit 2025: New capabilities for the predictive modeling of solids and nanomaterials

Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper presents the novel features and capabilities, both technical and scientific, which have been implemented over the past 5 years. This evolution occurred in the context of evolving hardware platforms, high-throughput calculation campaigns, and the growing use of machine learning to predict properties based on databases of first-principle results. We present new methodologies for ground states with constrained charge, spin, or temperature; for density functional perturbation theory extensions to flexoelectricity and polarons; and for excited states in many-body frameworks including GW, dynamical mean field theory, and coupled cluster. Technical advances have extended Abinit high-performance execution to graphical processing units and intensive parallelism. Second-principles methods build effective models on top of first-principle results to scale up in length and time scales. Finally, workflows have been developed in different community frameworks to automate Abinit calculations and enable users to simulate hundreds or thousands of materials in controlled and reproducible conditions.

Joint disruption of <i>Ret</i> and <i>Ednrb</i> transcription shifts cell fate trajectories in the enteric nervous system in Hirschsprung disease

Despite extensive genetic heterogeneity, 72% of pathogenic alleles for Hirschsprung disease (HSCR) arise from coding and regulatory variants in genes of the RET and EDNRB gene regulatory network (GRN) in the enteric nervous system (ENS). To elucidate the mechanisms leading to enteric neuronal loss from these genetic defects, we generated four strains of mice carrying reduced function alleles at Ret or Ednrb or both, along with their wild-type alleles. ENS tissue- and single-cell gene expression profiling of the developing and postnatal gastrointestinal tract in these five mouse models revealed three major insights: i) Ret and Ednrb deficiency, rather than complete loss, is sufficient to induce HSCR, ii) Ret and Ednrb demonstrate strong trans interactions, and iii) disruption of this interaction leads to cellular fate changes to compensate for neuronal loss. Critically, we show the combined reduction of signaling of these two receptors below a threshold in enteric neural crest-derived cells (ENCDCs) leads to a molecular tipping point at which otherwise lesser cellular defects result in aganglionosis. This study of targeted mouse models of a multifactorial disorder reveals how increasing dosage of genetic defects within a GRN leads to quantifiably increasing dysregulation from genotype to gene expression to cellular identity to function. Importantly, our studies establish that aganglionosis results only with severely reduced gene expression at both receptor genes and their consequent disruption of normal and compensatory cell fate trajectories.

Machine learning identifies lactate metabolism biomarkers and deciphers immune infiltration landscapes in Parkinson’s disease

From heteropolymer stiffness distributions to effective homopolymers. I. Theoretical modeling and computational verification

Synthetic copolymers and biopolymers, such as polypeptides and double-stranded DNA, often exhibit strong variations in bending stiffness along their contours, which can significantly impact conformational behavior at larger scales. To investigate these effects, we employ a discretized heterogeneous worm-like chain model, where the local persistence lengths are drawn from a Gaussian distribution. We develop a theoretical model that maps such heterogeneous chains to homogeneous chains with a single effective persistence length. For uncorrelated disorder, our model predicts that this effective stiffness is systematically smaller than the arithmetic mean of the local persistence lengths, indicating that flexible segments have a bigger influence on the overall chain stiffness than rigid segments. We validate our model predictions using off-lattice Monte Carlo simulations, considering both ideal and self-avoiding chains in a good solvent, and find excellent agreement in the regime where the persistence lengths are on the order of a few bond lengths, consistent with typical values observed in polypeptides.

HIF2α negatively regulates MYCN protein levels and promotes a low-risk noradrenergic phenotype in neuroblastoma

The role of HIF2α, encoded by EPAS1 , in neuroblastoma remains controversial. Here, we demonstrate that induction of high levels of HIF2α in MYCN-amplified neuroblastoma cells results in a rapid and profound reduction of the oncoprotein MYCN. This is followed by an upregulation of genes characteristic of noradrenergic cells in the adrenal medulla. Additionally, upon induction of HIF2α, the proliferation rate drops substantially, and cells develop elongated neurite-like protrusions, indicative of differentiation. In vivo HIF2α induction in established xenografts significantly attenuates tumor growth. Notably, analysis of sequenced neuroblastoma patient samples, revealed a negative correlation between EPAS1 and MYCN expression and a strong positive correlation between EPAS1 expression, high expression levels of noradrenergic markers, and improved patient outcome. This was paralleled by analysis of human developing adrenal medulla datasets wherein EPAS1 expression was prominent in populations with high expression levels of genes characteristic of noradrenergic chromaffin cells. Our findings show that high levels of HIF2α in neuroblastoma, leads to drastically reduced MYCN protein levels, cell cycle exit, and noradrenergic cell differentiation. Taken together, our results challenge the dogma that HIF2α acts as an oncogene in neuroblastoma.

Virulence factors, biofilm formation and antifungal resistance in Candida albicans from recurrent vulvovaginal candidiasis patients: a comparative study

Ultrafast proton transfer in a photoionized glycine by a mixed quantum–classical and quantum dynamics

We have theoretically investigated the ultrafast intramolecular hydrogen transfer in the glycine molecule after ionization, as observed by Castrovilli et al., J. Phys. Chem. Lett. 9, 6012–6016 (2018), following excitation of the molecule with an XUV attosecond pulse train of 1.5 fs duration. In this experiment, the interaction of the glycine molecule with the XUV pulse creates a superposition of electronic states, whose dynamics is coupled to the nuclear one. We employed the static exchange restricted active space density functional theory correlated approach, as implemented in the Tiresia code [Decleva et al., Molecules, 27(6), 2026 (2022)], to evaluate ionization probabilities. Coherence effects were studied through quantum dynamics simulations using the multi-layer multi-configuration time-dependent Hartree method on a vibronic coupling Hamiltonian model. Our findings indicate that, for the pulses used in the Castrovilli et al. experiment, electronic coherence dissipates very rapidly, in less than 3 fs. Consequently, we performed simulations starting from single electronic state. In addition, we described the long-term coupled electron–nuclear dynamics using the trajectory surface hopping method. Our results reveal that hydrogen transfer predominantly occurs when the active state reaches the cationic ground state. Charge analysis confirms that this process corresponds to a proton transfer.

Beyond adoption: The persistence of conservation and climate-smart agricultural practices in the United States

Achieving sustainability goals requires that humans change their behavior not just once but persistently. Yet despite decades of research on the adoption of conservation and climate-smart agricultural practices, little is known about the extent to which these practices persist over time. One key reason is the lack of longitudinal, field-level data. Using ground-verified, longitudinal data on cover cropping across thousands of farm parcels in Indiana (USA), we find that persistence is low and contrasts sharply with the predictions made by Indiana conservation experts. We also find low persistence in a new national dataset of self-reported cover cropping by farm operators. The potential for low behavioral persistence in sustainable agricultural practices raises essential questions about the design of conservation programs and the modeling and valuation of ecosystem services.

Spatial spillover effect and heterogeneity of digital economy on agricultural carbon emissions

Neural operators for forward and inverse potential–density mappings in classical density functional theory

Neural operators are capable of capturing nonlinear mappings between infinite-dimensional functional spaces, offering a data-driven approach to modeling complex functional relationships in classical density functional theory. In this work, we evaluate the performance of several neural operator architectures in learning the functional relationships between the one-body density profile ρ(x), the one-body direct correlation function c1(x), and the external potential Vext(x) of inhomogeneous one-dimensional hard-rod fluids, using training data generated from analytical solutions of the underlying statistical-mechanical model. Several variants of the Deep Operator Network (DeepONet) and the Fourier Neural Operator (FNO) were considered, each incorporating different machine-learning architectures, activation functions, and training strategies. These operator learning methods are benchmarked against a fully connected dense neural network, which serves as a baseline. We compared their performance in terms of the mean squared error loss in establishing the functional relationships as well as in predicting the excess free energy across two test sets: (1) a group test set generated via random cross-validation (CV) to assess interpolation capability and (2) a newly constructed dataset for leave-one-group CV to evaluate extrapolation performance. Our results show that FNO achieves the most accurate predictions of the excess free energy, with the squared ReLU activation function outperforming other activation choices. Among the DeepONet variants, the Residual Multiscale Convolutional Neural Network (RMSCNN) combined with a trainable Gaussian derivative kernel (GK-RMSCNN-DeepONet) demonstrates the best performance. Additionally, we applied the trained models to solve for the density profiles at various external potentials and compared the results with those obtained from the direct mapping Vext ↦ ρ with neural operators, as well as with Gaussian process regression combined with active learning by error control, which has shown strong performance in previous studies. While the direct mapping from Vext ↦ ρ suffers from high extrapolation error and proves inefficient for out-of-distribution predictions, the neural-operator mapping ρ ↦ c1 can effectively be used to solve the density profile via the Euler–Lagrange equation or be integrated with other surrogate methods. Moreover, neural operators offer additional flexibility through specialized operations, such as significance-based predictions on uneven grids (as in GK-CNN-DeepONet) and adaptive grid resolution adjustment (as in FNO), both of which can enhance prediction accuracy.

Mutant p53 regulates cancer cell invasion in complex three-dimensional environments through mevalonate pathway–dependent Rho/ROCK signaling

Certain TP53 mutations can confer neomorphic gain of function (GOF) activities to the p53 protein that affect cancer progression. Yet the concept of mutant p53 GOF has been challenged. Here, using various strategies to alter the status of mutant versions of p53 in different cell lines, we demonstrate that mutant p53 stimulates cancer cell invasion in three-dimensional environments. Mechanistically, mutant p53 enhances RhoA/ROCK-dependent cell contractility and cell-mediated extracellular matrix (ECM) reorganization via increasing mevalonate pathway–dependent RhoA localization to the membrane. In line with this, RhoA-dependent proinvasive activity is also mediated by IDI-1, a mevalonate pathway product. Further, the invasion-enhancing effect of mutant p53 is dictated by the biomechanical properties of the surrounding ECM, thereby adding a cell-independent layer of regulation to mutant p53 GOF activity that is mediated by dynamic reciprocal cell–ECM interactions. Together our findings link mutant p53 metabolic GOF activity with a context-dependent invasive cellular phenotype.

Assessment of corrosion restraint effect of carbon steel immersed in hydrochloric acid by expired tilmicosin drug

Abstract This research investigates the application of Expired Tilmicosin Drug as a corrosion inhibitor for C-steel in a 1 M HCl solution. FT-IR measurements, atomic force microscopy (AFM), electrochemical impedance spectroscopy (EIS), weight loss (WL), and potentiodynamic polarization (PDP) were employed to evaluate the efficacy of Expired Tilmicosin Drug in protecting C-steel against corrosion. According to the findings, the inhibition efficiency (% IE) increased as the Expired Tilmicosin Drug concentration increased and reached 91.8% at 300 ppm, 25 °C, but decreased to 85.6% at 45 °C. The investigated drug acted as a mixed–kind inhibitor from the data of PDP technique. The Langmuir adsorption model was supported by the drug’s adsorption behavior on the C-steel surface. The adsorption phenomena was found to be spontaneous based on the computed values of the standard free energy change of adsorption (ΔG o ads ). Fourier transform infrared spectroscopy (FT-IR) and atomic force microscopy (AFM) demonstrated that the drug molecules had a strong bond with the C-steel surface. Density functional theory (DFT) calculations and molecular dynamics (MC) simulations provided further insight into the chemical interactions between Expired Tilmicosin Drug and the C-steel surface. This study confirms that there is agreement between experimental and theoretical results. This research introduces the novel application of Expired Tilmicosin Drug, highlighting its non-toxic nature and cost-effectiveness, making it a promising alternative for corrosion prevention in industrial applications. This study investigates the dual role of Expired Tilmicosin Drug in addressing expired pharmaceutical waste and developing an efficient corrosion inhibitor for C-steel in acidic environments. Repurposing Expired Tilmicosin Drug provides a sustainable solution to environmental hazards while demonstrating high corrosion inhibition efficiency.

Accelerating self-consistent field theoretic simulations for disordered systems with deep learning

Polymer science holds a pivotal role in areas such as advanced materials design, drug delivery systems, and biological systems, where being able to efficiently predict polymer thermodynamics and self-assembly is crucial. Self-consistent field theory (SCFT) offers a theoretical framework with many successful predictions that have guided experiments. However, there are classes of systems that are challenging to describe with SCFT due to their computational expense, such as anisotropic systems and worm-like chain models. In this study, we take a first step toward alleviating these challenges by developing a machine-learning approach that can predict density fields directly from the potential fields without the need for computing chain propagators, which is typically the computationally demanding step of a field theory. By integrating different types of neural network models into SCFT, we compared the performance of the models and developed a robust and computationally efficient model for Gaussian chain models that form disordered, inhomogeneous (microphase separated) structures. Our model is able to achieve a speedup of more than three times for the same size systems and up to 100 times for larger systems in our tested systems. The results of this work demonstrate one strategy for how deep learning can be leveraged to improve the efficiency of large-scale SCFT simulations, and the methods herein could be readily extended to other, more computationally demanding models.

Capillary self-thinning of threads of polyelectrolyte solutions with axial electric fields

A theory of solutions of charged polyelectrolyte (PE) macromolecules, treating them as electric dipoles, is proposed. In a thin thread of PE solution sustained between two disks—whether wettable or nonwettable—these dipoles are reoriented by an axial electric field, aligning themselves with the field direction. This alignment causes significant axial elastic stresses and affects capillary self-thinning dynamics of the thread, slowing the process and potentially arresting it entirely, and even leading to oscillatory regimes. Accordingly, the evolution of the thread radius deviates significantly from the exponential decay characteristic of solutions of flexible polymer macromolecules and the linear decay characteristic of Newtonian fluids. At relatively high electric field strengths, in a thread where elastic stresses become dominant, an oscillatory regime emerges. Here, the cross-sectional radius not only decreases but also increases and oscillates in time—a behavior rooted in an ill-posedness of the problem reducing to the Laplace equation in case of strong electric fields. This indicates a manifestation of Hadamard instability. The theory is supported by experimental data acquired in this work.

Mapping spatiotemporal variations in lakeside populations for sustainable lake management

Erratum: “Third density and acoustic virial coefficients of helium isotopologues from <i>ab initio</i> calculations” [J. Chem. Phys. 160, 244305 (2024)]

Cytosolic proliferating cell nuclear antigen (PCNA) orchestrates neutrophil hyperactivation in COVID-19

Neutrophils are central mediators of the hyperinflammatory response in severe SARS-CoV-2 infection. We report elevated cytosolic levels of proliferating cell nuclear antigen (PCNA) in neutrophils from patients with severe and critical COVID-19, correlating with enhanced NADPH oxidase–dependent reactive oxygen species (ROS) generation and neutrophil extracellular trap (NET) formation. Using T2AA, a small-molecule inhibitor of the PCNA scaffold, we demonstrate potent suppression of NADPH oxidase activation and NET release, particularly in response to SARS-CoV-2 RNA. Mechanistically, we identify a previously unrecognized interaction between PCNA and the heterodimeric S100A8/S100A9 (calprotectin), predominantly enriched in CD16 high CD62L low neutrophils expanded during COVID-19. PCNA binds the dimeric S100A8/S100A9 complex mediated via S100A8 subunit with micromolar affinity, and this interaction is abrogated by tetramerization, suggesting regulation by intracellular calcium. Disruption of this complex by T2AA inhibited ROS production in an S100A8/S100A9-dependent manner, implicating calprotectin as a functional regulator of neutrophil activation. In a betacoronavirus mouse model, T2AA treatment attenuated lung inflammation, reduced NET and calprotectin levels, and shifted pulmonary neutrophils away from hyperactivated and immunosuppressive phenotypes, consistent with immune reprogramming toward resolution. These findings establish cytosolic PCNA as a central scaffold in neutrophil hyperactivation during COVID-19 and highlight its pharmacological disruption as a promising host-directed strategy to limit inflammation and prevent organ damage.

RNA-seq analysis of wild-type and mutated TBPL1 gene in breast cancer cells lines through CRISPR/Cas9 approach reveals novel molecular signatures

Modeling the P3HT microcavity reflectance spectrum: Introducing a partitioning scheme for treating large disordered chromophore ensembles

Analysis of the microcavity reflectivity spectrum of a thin poly(thiophene) (P3HT) film is presented, based on the Frenkel–Holstein–Tavis–Cummings Hamiltonian and a Lindblad formalism to describe relaxation through system–bath interactions. A partitioning scheme is employed to treat large, disordered ensembles of P3HT chain segments, which, based on analysis of the free-space absorption spectrum, is divided into aggregate (60%) and amorphous (40%) domains. The reflectivity spectrum is in excellent agreement with the measured spectrum when the ensemble light–matter coupling, NgS, is taken to be 0.9 eV, where N is the total number of P3HT chain segments (in the aggregate and amorphous domains) and gs is the light–matter coupling for a single segment. The spectrum exhibits a relatively narrow lower polariton (LP) feature with a much broader upper polariton (UP), with an approximate Rabi splitting of 1 eV. The two relatively weak middle polaritons are attributed to bright vibronic polaritons, which owe their spectral appearance to a Herzberg–Teller mechanism in which exciton–phonons with zero quasi-momentum borrow optical intensity from the LP and UP. The spectral feature attributed to the LP originates mainly from aggregate domains, while the UP originates from both aggregate and amorphous domains.