Browse Articles

Extrapolating molecular dynamics simulations to zero time step and across thermodynamic space

The integration time step is a critical determinant of performance in molecular dynamics simulations, governing the trade-off between speed and fidelity. Although 2 fs remains the standard in atomistic biomolecular simulations, the push for performance has popularized a 4 fs time step with hydrogen mass repartitioning, often combined with multiple time stepping or mass rescaling. However, it is often unclear whether a chosen protocol is overly aggressive, as the apparent numerical stability of a trajectory can mask underlying thermodynamic inaccuracies. Increasing the time step will exacerbate systematic discretization errors, inherent to all numerical integration algorithms. In the widely used Verlet family of integrators, these errors manifest as O(Δt2) deviations in thermodynamic observables such as potential energy and volume, and for common Langevin splitting schemes, even temperature. We demonstrate that these deviations follow a simple, linear thermodynamic model, allowing for their rigorous removal by extrapolation to the zero time step limit. In turn, the time-step dependence provides us with estimates of the system heat capacity, compressibility, and thermal expansion coefficient. This framework allows us to construct consistent probability distributions of energy and volume across thermodynamic states, effectively recovering Boltzmann-consistent statistics at a target condition independent of time step. These considerations are particularly important for enhanced sampling methods, such as replica exchange and umbrella sampling, which rely on rigorous Boltzmann sampling and require accurate energies and temperatures for valid replica exchange probabilities and statistical reweighting.

Correction for Kristal et al., Signing at the beginning versus at the end does not decrease dishonesty

A FUCA based MCDM model with complex q rung orthopair fuzzy information for evaluating physical education programs

How to quantify long-time rotational motion in molecular systems

We show that all existing methods quantifying rotational motion in molecular fluids eventually have severe limitations in systems undergoing complex rotational motion characterized by slow, heterogeneous, or intermittent dynamics. This impacts, in particular, the study of rotational dynamics in molecular supercooled liquids near their glass transition, as well as discussions of the decoupling between rotational and translational motion and violations of the Debye–Stokes–Einstein relation. We present a brief overview of existing methods and explain why none of them can accurately capture the evolution of rotational dynamics from a diffusive fluid to an arrested solid, thus resolving inconsistent literature results. We then introduce an empirical method that efficiently solves all issues. We benchmark our method by devising a family of continuous-time random walk models for rotational dynamics. Our method correctly quantifies the statistics of free and caged rotational motion, as well as non-Gaussian and non-Fickian rotational dynamics, and should allow a better characterization of dynamic heterogeneity in the rotational motion of supercooled molecular fluids.

The dynamic genomes of <i>Salvinia</i> reshape our understanding of fern chromosome evolution

Ferns are well known for their exceptionally large genomes and high chromosome numbers, which may be in part due to whole genome duplications (WGDs) followed by slow diploidization. To better understand the mode of fern genome evolution, we focus on the heterosporous fern genus Salvinia, which exhibits striking variation in genome size and chromosome number. We generated chromosome-level genome assemblies for Salvinia cucullata, the fern with the smallest genome, and Salvinia molesta, a globally invasive species widely thought to be an allopentaploid. Surprisingly, we found that S. molesta is in fact a diploid hybrid and that S. cucullata , despite having a genome ten times smaller than S. molesta , has substantially more chromosomes. Both species lack any recent WGDs and their highly variable genomes were predominately shaped by transposable element proliferation and chromosome rearrangements. The complete decoupling of chromosome number and genome size in Salvinia sharply contrasts the typical pattern in ferns, which are mostly homosporous and produce only one type of spore by meiosis. Many of the genome features observed in Salvinia are consistent with genomic changes due to female meiotic drive, a mechanism possible only in heterosporous plants that produce distinct microspores and megaspores. These results redefine the genetic identity of S. molesta and provide insights into its invasive success. The marked variation in genome composition and structure within Salvinia challenges the prevailing model of fern genome evolution while aligning with expectations for angiosperms, another heterosporous lineage.

Exercise intention, positive body image, and mental health and well-being among elite young adult athletes in china: a network analysis incorporating gender differences

Abstract Elite young adult athletes’ mental health is a critical global sports governance priority, yet existing research is limited by linear models, Western-centric samples, and insufficient exploration of gender-specific structural patterns among psychological constructs. We applied Gaussian and Bayesian network analyses to 444 Chinese elite young adult athletes aged 18–25 years to investigate interconnections between exercise intention, positive body image, subjective well-being, and psychological distress, along with the potential gender differences in the relationships. Results showed no variation in global network strength ( p  = 0.38) but significant structural disparities ( p  = 0.04), with happiness as the most central node across both genders. Body appreciation was negatively associated with depression in females but not in males, and this gender difference was significant ( p  = 0.03). In males, the Bayesian network retained an indicative probabilistic configuration connecting exercise intention, happiness, and body appreciation, whereas this pattern was absent in females. No probabilistic directional connections from exercise intention or body appreciation to depression or anxiety were retained in either gender. This study provides a system-level understanding of gender-specific psychological network patterns in Chinese elite young adult athletes, with findings that may serve as hypotheses for future longitudinal and applied studies and inform gender-sensitive mental health considerations in sport.

Classically driven hybrid quantum algorithms with sequential Givens rotations for reduced measurement cost

Quantum algorithms for electronic-structure simulations are actively being developed, yet many hybrid quantum–classical approaches are bottlenecked by the measurement overhead associated with large molecular Hamiltonians. Here, we introduce a diagonalization-driven framework that progressively drives the electronic Hamiltonian toward a (block-)diagonal form in the Slater-determinant basis using sequential Givens rotations. In contrast to Schrödinger-picture methods that variationally optimize a wave function, our approach adopts a Heisenberg-picture viewpoint: the Hamiltonian is iteratively transformed, and rotation angles are determined classically from low-dimensional effective blocks, reducing the quantum workload to a small, fixed set of matrix-element measurements per iteration. Candidate generators are estimated via approximate Baker–Campbell–Hausdorff updates with truncation and cumulant-based approximations that control Hamiltonian growth, complemented by stochastic selection to avoid stagnation. We further introduce an angle-merging procedure that reduces circuit depth by consolidating repeated small-angle rotations. We benchmark the framework on N2 and strongly correlated hydrogen systems, assessing convergence behavior, residual-structure diagnostics, measurement–accuracy trade-offs, circuit costs, and robustness under finite sampling.

The mechanism for ligand activation of the Smoothened G protein–coupled receptor

The Smoothened (SMO) G protein–coupled receptor is a key part of the Hedgehog (Hh) signaling pathway, and it is an oncoprotein that is an important target for understanding cancers such as basal cell carcinoma. However, its mechanism of activation remains unknown. To this end, we investigate here the sequence of G protein and cholesterol (CHL) ligand binding to SMO on the pathway toward activation. Our results are consistent with the G protein–first activation pathway of SMO in the Hh signaling pathway. In this model, CHL first binds to the cysteine rich domain (CRD) of the inactive SMO, which remains inactive at this stage. The G protein can then spontaneously bind to this SMO, forming a precoupled complex that remains inactive while Patched (PTCH) is attached to the membrane. Upon binding of the Hh ligand to PTCH, the CHL levels in the membrane increase, enabling CHL to also bind to the transmembrane domain (TMD) of SMO. With CHL occupying both the CRD and the TMD sites, opening of the G protein becomes energetically favorable, promoting GDP release and initiating downstream G-protein signaling.

Association of the CHG index combined with obesity indices and incident cardiometabolic multimorbidity in a nationwide prospective cohort study

Revisiting crossed-correlated baths in open quantum systems simulated by HEOM or T-TEDOPA

Excited-state dynamics of open quantum systems is analyzed by the hierarchical equations of motion (HEOM) or the thermalized time-evolving density operator with orthogonal polynomials algorithm (T-TEDOPA) method when a discrete ab initio linear vibronic model is parameterized by continuous temperature-dependent spectral densities leading to crossed correlation functions, i.e., correlated fluctuations of the energy gap collective modes. We focus on a conical intersection involving two collective modes tuning the energy of each excited state, and we revisit the transformation of the initial correlated tuning baths to de-correlated shared baths in order to reduce the computational resources. While a completely frequency-dependent transformation poses problems for HEOM, we find that in some particular cases, an optimal approximate frequency-independent transformation may be derived. On the contrary, T-TEDOPA is very efficient and allows us to use this frequency-dependent transformation at the price of managing long-range couplings in the tensor chain. An illustrative application is shown by using the linear vibronic coupling model of a planar symmetrical (phenylethynyl)benzene dimer.

The emergence of novel versus known three-dimensional structures from random sequences

It has been hypothesized that while random sequences are unlikely to fold into proteins of the length of globular proteins, repeated random sequences are more likely to adopt stably folded structures, with implications for molecular evolution. We used structure prediction methods to determine the foldability of approximately 120-residue sequences composed of 5- to 60-residue random repeats. With repeats of less than 30-residues, sequences were frequently discovered (1 to 12%) that fold with high confidence. For less than 60-residue repeats, we frequently observe β-solenoids, similar to those seen in natural proteins. We observe solenoids stabilized by apolar packing as well as ones stabilized by polar interactions with Ca 2+ in the core of the structure as in natural Repeats in ToXin (RTX) domains. Helical bundles were observed with high frequency when insertions or deletions were included between blocks of repeating sequences. We also observed a new supersecondary structure consisting of a tightly wound α-helical screw and experimentally confirmed its stability and structure by circular dichroism (CD) spectroscopy and X-ray crystallography. Thus, structure predictors can discover structures that are well out of the distribution of the data upon which they were trained. Beyond 40-residue repeat lengths, very few sequences were predicted to fold. The small number of structures we observed was representative of well-established major classes of tertiary structures; greater sampling would be needed to discover novel structures from a random distribution. These studies illuminate dark matter regions of protein structure space and support previous predictions that proteins evolved through the assortment of shorter peptide sequences.

Integrated lab-scale greywater filtration and soil aquifer treatment for sustainable groundwater recharge

Nitrogenation of microscopic MoS2 surfaces by oxidation scanning probe lithography

MoS2 has found many applications in optoelectronics, energy harvesting, and catalysis due to its unique properties and functionalities. It has been shown that its properties can be tuned by thermal oxidation and plasma treatment. Herein, we examined the capability of oxidation scanning probe lithography (o-SPL) for direct nitrogenation of the MoS2 crystals under ambient conditions. By utilizing Kelvin probe force microscopy together with Auger electron spectroscopy, we found out that the o-SPL method was able to simultaneously oxidize and nitrogenize MoS2 flakes when a relatively high input voltage was exerted. Under such harsh conditions, oxygen and nitrogen atoms were incorporated and replaced the sulfur atoms within MoS2. At the same time, large surface topographical changes were observed mostly due to sample delamination. On the contrary, low input voltage was able to produce large topographical changes associated only with desulfurization but without any oxidation/nitrogenation. Finally, high voltage o-SPL treatment of MoS2 samples pre-oxidized in air also produced their nitrogenation coupled with oxidation.

A general framework for predicting the ecological effects of range expansions in marine systems

Warming global temperatures are driving species to shift beyond their historical geographic ranges into novel, expanded ranges, where they may have both positive and negative effects on recipient populations. Although no framework currently exists to anticipate these effects, invasion biology theory suggests expanders’ effects may be predictable from expanders’ historical roles and trophic positions. We conducted a global synthesis of over 1,200 population-level effects of marine range-expanding species reported across 1,075 studies. We hypothesized that, similar to invasive species, expanding species’ impacts could be predicted by (H1) their ecological roles in their historical ranges and (H2) their trophic levels. We found that effects in these species’ historical ranges reliably predict their impacts in expanded ranges, but that, overall, marine range expanders tend to be more detrimental—or less beneficial—in novel communities. Higher trophic level expanders tend to have stronger effects (both positive and negative) on resident species than lower trophic level expanders. Effect magnitudes were further modulated by the type of interaction (e.g., predation, competition, physical disturbance, etc.), with the strongest effects arising from indirect interactions. Finally, we found that native producers experienced some of the strongest effects when compared to humans, vertebrates, or invertebrates. Together, our results indicate that existing knowledge of species’ roles are key to anticipating which climate-driven range expansions are likely to have the largest effects on recipient communities.

Correction: Optimization of linear attenuation coefficients and characterization of mechanical and thermal properties in silica ash-reinforced PDMS composites

Daily briefing: Three decades of Dolly

Deep neural networks as discrete dynamical systems: Implications for physics-informed learning

We revisit the analogy between feed-forward deep neural networks (DNNs) and discrete dynamical systems derived from neural integral equations and their corresponding partial differential equation forms. A comparative analysis between the numerical/exact solutions of the Burgers’ and Eikonal equations and those obtained via physics-informed neural networks (PINNs) is presented. We show that PINN learning provides a different computational pathway compared to standard numerical discretization in approximating essentially the same underlying dynamics of the system. Within this framework, DNNs can be interpreted as discrete dynamical systems whose layerwise evolution approaches attractors, and multiple parameter configurations may yield comparable solutions, reflecting the degeneracy of the inverse mapping. In contrast to the structured operators associated with finite-difference procedures, PINNs learn dense parameter representations that are not directly associated with classical discretization stencils. This distributed representation generally involves a larger number of parameters, leading to reduced interpretability and increased computational cost. However, the additional flexibility of such representations may offer advantages in high-dimensional settings where classical grid-based methods become impractical.

Genomic and structural evidence of SARS-CoV-2 and MERS-CoV in migratory birds

Migratory birds are the natural reservoir of influenza A virus (IAV), but their role as a carrier of SARS-CoV-2 remains unclear. Here, we report the identification of three almost full-length viral genome sequences of SARS-CoV-2 variants of concern (VOCs) in Tundra swans. These sequences are named hCoV-19/Tundra swan/Jiangxi/IMCAS_M1/2021 (IMCAS_M1), hCoV-19/Tundra swan/Jiangxi /IMCAS_M2/2021 (IMCAS_M2), and hCoV-19/Tundra swan/Jiangxi/IMCAS_M3/2021 (IMCAS_M3). IMCAS_M1 and IMCAS_M3 have the same mutations as the Beta VOC (K417N, E484K, and N501Y) in the receptor-binding domain (RBD) of the viral spike (S) protein, whereas IMCAS_M2 shares the same mutations as the Gamma VOC (K417T, E484K, and N501Y) in the RBD with all three showing their distinct mutations in the genomes. Virus receptor angiotensin-converting enzyme 2 (ACE2) proteins from both Tundra swan (tsACE2) and Black swan (bsACE2) can bind to the RBDs of all three viruses and the Alpha VOC, but not to RBD of the prototype (PT) virus. The polar contacts and hydrophobic interactions revealed by cryo-electron microscopy (cryo-EM) structures of the RBD–ACE2 complex, play key roles in virus–receptor engagement. Furthermore, HeLa cells expressing bsACE2 and tsACE2 proteins could be transduced by pseudotyped SARS-CoV-2 variants (Alpha, Beta, and Gamma) but not PT SARS-CoV-2. In addition, we obtained one partial genome of MERS-CoV named Bar-headed goose/Tibet/IMCAS_M4/2022 (IMCAS_M4) with 20,180 bp (~70.0% coverage). Our findings highlight the importance of migratory birds as potential carrier of both SARS-CoV-2 and MERS-CoV, thereby posing potential threat to public health.

Local Ecological Knowledge offers opportunities to study and monitor elusive carnivores in coexistence landscapes

Vesicle size and membrane composition control monomer transfer pathways in multicomponent lipid vesicles

Lipid exchange between populations of non-fusing protocells, mediated by monomer diffusion through the aqueous phase, is a key process underlying protocell growth and compositional equilibration. While most theoretical descriptions consider single-component membranes with constant desorption rates, the influence of vesicle size and lipid composition on lipid-transfer dynamics remains poorly understood. Here, we develop a kinetic model for lipid exchange in multicomponent protocell populations that incorporates lipid species with different desorption rates and composition-dependent membrane packing effects. We show that lipid composition heterogeneity introduces multiple equilibration timescales and can generate non-monotonic dynamics, including transient overshoots of intermediate lipid species. Vesicle-size asymmetry controls the direction and rate of lipid transfer, determining the donor or acceptor character of individual vesicle populations. Membrane compression further modifies lipid-transfer kinetics and vesicle-area evolution. These results demonstrate how vesicle size, lipid composition, and membrane packing jointly govern lipid-transfer dynamics in heterogeneous protocell populations.