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Modulating TPP riboswitch activity simultaneously enhances crop yield, nutritional quality and stress tolerance
Benchmarking of vibrational exciton models against quantum-chemical localized-mode calculations
Vibrational exciton models are widely used for the simulation of biomolecular vibrational spectra, particularly two-dimensional infrared spectra. The parameters entering such models, specifically harmonic local-mode frequencies and harmonic coupling constants, are provided by vibrational maps, which have been parameterized against computational data for small molecules as well as experimental data. Here, we put forward a novel approach for assessing the quality of these harmonic vibrational maps against quantum-chemical reference data. For a test set consisting of molecular dynamics snapshots of polypeptides and small proteins, covering different secondary structure motifs, we performed full quantum-chemical calculations of harmonic vibrational frequencies and normal modes and applied a localization of normal modes to obtain localized-mode frequencies and coupling constants. These can be directly compared to those predicted by vibrational maps. We find that while there is a good correlation for the coupling constants and for local-mode frequencies of isolated polypeptides, there is hardly any correlation for the local-mode frequencies of solvated polypeptides. This striking finding calls into question the accuracy of the electrostatic maps that are used to model the effect of the solvent molecules on local-mode frequencies.
HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps
Abstract Tissues are organized through the assembly of diverse cell types into multicellular structures that exhibit hierarchical spatial organization. We present HRCHY-CytoCommunity, a graph neural network framework for identifying multi-level tissue structures directly from cell-type annotated spatial maps. It integrates differentiable graph pooling, adaptive edge pruning, and consistency and balance regularization in an end-to-end model, simultaneously inferring robust structures across multiple scales while preserving complete cellular coverage and fully nested relationships. The framework also supports cross-sample hierarchy alignment via cell-type enrichment-based clustering. Benchmarking on diverse spatial omics datasets, HRCHY-CytoCommunity outperforms existing hierarchical and non-hierarchical methods in identifying both coarse-grained tissue compartments and fine-grained cellular neighborhoods. Applied to a breast cancer cohort with clinical outcomes, the framework enables hierarchical prognostic stratification of patients and reveals survival-associated spatial patterns. HRCHY-CytoCommunity represents a general and scalable tool for deciphering tissue organization from single cells to multicellular modules, and ultimately to intact tissues and organs.
Quantum dynamics study of photodissociation in phenol–water clusters
The photodissociation of hydrogen atoms from phenol–(H2O)n (n = 0, 1, 2) clusters was investigated using the direct dynamics variational multi-configurational Gaussian quantum dynamics method paired with the SA(4)-CAS(10,10)SCF/6-311+G** level of theory. All vibrational modes were included in the simulations. Hydrogen bonding of the phenol to water molecules changes the character of the 1πσ* excited state, which in turn pushes the ππ*–πσ* state crossing up in energy. This results in a lower probability of H atom dissociation from the ππ* state with increasing solvation. Dissociation from 1πσ*, as well as the effect of vibrational excitation in the phenol molecule, was also investigated.
Conditional BCL-2 Expression in Fibroblasts Promotes Persistent Pulmonary Fibrosis which is Reversible by Therapeutic BCL-2 Inhibition
Diffusion-controlled reactions on an active site in a spherical cavity: Extension of Berg’s theory
This study is due to various applications in physics, chemistry, and especially biology, where both the bounded configuration domain and chemical anisotropy could play a great part. In fact, we generalize the well-known Berg’s theory, which describes diffusion-controlled reactions occurring within a spherically symmetric absorber-cavity system. The local concentration and the reaction trapping rate at which a small diffusing particle is captured by an axially symmetric one-reactive-patch absorber inside a spherical cavity were found semi-analytically and numerically by means of the dual series relations method. This approach leads to such incredibly fast convergence that it may rightly be referred to as an exact one. The results obtained can be used to test numerical programs that describe diffusion-controlled reactions in real physical systems for reactants with arbitrary anisotropic reactivity, which are located inside various cavities as well as in the unbounded domains. Moreover, we managed to find a close connection between the dual series relations method and the generalized method of separation of variables.
Heterogeneously integrated lithium tantalate-on-silicon nitride modulators for high-speed communications
Abstract Ultrabroadband integrated modulators involving materials beyond those available in silicon manufacturing increasingly rely on the Pockels effect. Among electro-optic materials, lithium tantalate offers comparable Pockels coefficients to lithium niobate but with significantly improved photostability, lower birefringence, higher optical damage threshold, and enhanced DC bias stability. Here we demonstrate wafer-scale heterogeneous integration of lithium tantalate films on low-loss silicon nitride photonic integrated circuits, achieving low optical losses ( ~ 14.2 dB/m) while combining the mature processing of silicon nitride waveguides with the ultrafast electro-optic response of thin-film lithium tantalate. The resulting devices achieve a 6 V half-wave voltage, and support modulation bandwidths of up to 100 GHz. We use single intensity modulators and in-phase/quadrature (IQ) modulators to transmit PAM4 and 16-QAM signals reaching up to 333 and 581 Gbit/s net data rates, respectively. Our results establish lithium tantalate-on-silicon nitride as a viable platform for RF photonics, interconnects, and analog signal processing.
Two-dimensional infrared spectroscopy of solute–solvent complexes from linear-scaling DFT and machine learning
Two-dimensional infrared (2DIR) spectroscopy captures vibrational correlations on femtosecond timescales, offering direct insights into hydrogen(H)-bonding dynamics and other ultrafast molecular processes. However, interpreting these spectra requires simulations that accurately describe solute–solvent interactions over realistic timescales and system sizes. While classical approaches using empirical frequency maps are common, ab initio molecular dynamics (AIMD) offers a more rigorous alternative by treating dynamics and vibrational frequencies on a consistent theoretical level. The primary drawback of AIMD is its high computational cost, which typically limits simulations to short trajectories. Here, we introduce a hybrid strategy that combines linear-scaling density functional theory (LS-DFT) with a machine-learned (ML) interatomic potential. We use short LS-DFT simulations to generate reference energies, forces, and electron-density-derived dipole moments, which then serve as training data for a DeepMD model. The resulting ML potential allows nanosecond-scale dynamics at a fraction of the ab initio cost. We demonstrate this approach for N-methylacetamide in methanol, a model system known to form distinct H-bonded subpopulations. A key advantage of our method is that it bypasses the need for empirical frequency maps. Instead, molecular dipoles are learned directly from the electron density, and instantaneous vibrational frequencies are calculated from stable numerical Hessians. The resulting linear and 2DIR spectra show excellent agreement with experiment, accurately reproducing the characteristic doublet structure of the amide I band. This framework provides a practical and accurate route to simulating vibrational spectra at the AIMD level of theory for a wide range of IR-active solutes and H-bonded complexes.
Hyperparametric solitons in nondegenerate optical parametric oscillators
Abstract Dissipative solitons and their frequency combs hold great potential for applications in optical communications, spectroscopy, precision time-keeping and beyond. Recent demonstrations based on the combination of second-harmonic generation and degenerate optical parametric oscillators (OPOs) show the interest in shifting soliton spectra away from the telecom’s C-band pump sources. However, these approaches lack the tunability offered by nondegenerate OPOs. This work presents a proof-of-principle demonstration of solitons in a nondegenerate OPO system based on a silicon-nitride microresonator, with engineered dispersion and optimised coupling rates. By pumping a relatively low-Q resonance in the C-band, we excite a signal soliton comb centred around a far-detuned, high-Q, O-band resonance, as well as repetition-rate-locked combs at the pump and idler frequencies, with the latter occurring at a wavelength beyond 2 μm. The solitons supported by this platform — hyperparametric solitons — are distinct from other families of dissipative solitons, as they emerge when the narrow-band signal mode, phase-matched under negative pump detuning, reaches sufficient power to drive bistability in the parametric signal. We investigate the properties of hyperparametric solitons, including their parametrically generated background and multisoliton states, both experimentally and through theoretical modelling.
Chlorine–sulfur isomers as parents of ClS2 and SCl2 on Venus: Spectroscopy and photochemistry of ClSSCl, SSCl2, and (ClS)2
Chlorine–sulfur photochemistry has emerged as a key component of Venus’s complex atmospheric chemistry and a promising avenue for explaining the planet’s sulfur cycle. A theoretical study of the ClSSCl, SSCl2, and (ClS)2 isomers has been performed to elucidate their stability, spectroscopy, and photochemistry, with implications for their potential presence in Venus's upper atmosphere. The ClSSCl and SSCl2 isomers are thermodynamically stable, with significant Cl–S and S–S bond dissociation energies (>47 kcal/mol), suggesting resistance to thermal dissociation. In contrast, the cyclic (ClS)2 isomer is a metastable species with a weak Cl–S bond, indicating it is likely a transient intermediate or pre-reaction complex. Excited states and photoabsorption cross section analysis reveal that ClSSCl exhibits a strong UV absorption around 240 nm, resulting in specific, rapid photodissociation channels. Conversely, SSCl2 displays broad absorption across the near-UV–visible range (∼340 nm), with a high density of interacting states, leading to complex and slow photodissociation dynamics. These results establish ClSSCl and SSCl2 as plausible candidates for detection in the Venusian atmosphere and, critically, as potential photochemical parent molecules for the ClS2 and SCl2 species, providing an accurate spectroscopic and photochemical roadmap for their future observation and simulation.
Scaling laws in confined media applied for biomarker detection
Accelerating global search of gold–silver clusters using equivariant graph neural network
Medium-sized gold–silver clusters have been relatively underexplored due to the computational complexities associated with density functional theory (DFT) calculations and the intricate nature of their potential energy surfaces. Recently, graph neural networks (GNNs) have emerged as efficient tools for fitting these potential energy surfaces, providing both rapid computation and high accuracy. Equivariant GNNs, which incorporate vector features of nodes, are particularly adept at extracting more complex and abstract information without significantly increasing the computational burden. In this study, we develop an equivariant GNN named CCCNet that requires only coordinate and elemental information as input. This model, trained on over 1.4 × 106 cluster structures and tested on independent compositions, achieves high prediction accuracy for binding energies (MAE = 6.5 meV/atom) and atomic forces (MAE = 25.4 meV/Å). By integrating our CCCNet with a comprehensive genetic algorithm (CGA) software framework, we successfully conducted searches for global minimum structures of AumAgn clusters (where m + n = 20, 24, 30). The computational cost is remarkably less than conventional DFT calculations by about three orders of magnitude, showing the power of equivariant GNNs for accelerating structural discovery in medium-sized clusters. Several previously unknown low-energy configurations were uncovered and novel structural motifs that differ markedly from the established growth patterns were revealed. Therefore, our findings provide new insights into the stability and design principles of Au–Ag nanoclusters.
Coordination restraint of Rh-Cu diatomic catalyst and C-H bond oxygen insertion for methanol synthesis
SOD1 lactylation impair its enzymatic activity by conformational change to aggravate intervertebral disc degeneration
Vertical nanodiamond dominated sheets possessing both high capacitance and high n-type Hall mobility
Understanding pre-training data effects in retinal foundation models using two large fundus cohorts
Abstract Medical foundation models, pre-trained on large-scale unlabelled data, show strong performance and data efficiency when adapted to various clinically relevant applications. However, how pre-training data shape the generalisability and fairness of these models remains unexplored. Here we address this using two cohorts from Moorfields Eye Hospital (UK) and the Shanghai Diabetes Prevention Program (China), each containing 904,170 fundus photographs for model pre-training. Using identical pipelines, we train parallel foundation models using individual cohort and evaluate them on downstream tasks with publicly available datasets and held-out data from each site. The parallel models show competitive performance to data that differ substantially from their pre-training data. Nevertheless, we observe fairness gaps over age subgroups, whereas sex and ethnicity show minimal impact. These results demonstrate the good generalisability of retinal foundation models and indicate that pre-training demographic attributes shape fairness differently, highlighting the importance of domain-specific, fine-grained data curation for efficient foundation model development.
Doublet microtubule-associated tektins and enzymes differentially regulate sperm flagellar integrity and motility
Abstract Doublet microtubule (DMT)-associated proteins assemble and drive sperm flagella, which are essential for successful fertilization. However, the exact roles of different DMT-associated proteins in sperm function and the underlying molecular mechanisms remain elusive. Here, we generate four gene-knockout mice based on high-resolution structures targeting distinct DMT components: two intermediate filament-like tektins (TEKT1, TEKT5) and two enzymes (TSSK6, DUSP21). The depletion of TEKT1, shared by sperm flagella and motile cilia, causes male infertility characterized by impaired sperm motility and loss of the tektin bundle, whereas sperm-specific Tekt5 knockout (KO) mice remain fertile with largely normal flagellar function, indicating functional divergence within the tektin family. Tssk6 KO spermatozoa exhibit severely disturbed morphology and motility, resulting in homozygote infertility and heterozygote subfertility. Phosphoproteomics reveals dysregulated phosphorylation of axonemal proteins, highlighting the critical role of kinase-mediated signaling in regulating sperm motility. Conversely, Dusp21 KO mice display no fertility or sperm motility defects, suggesting compensatory phosphatase activity. Phenotypic comparisons between Tekt1 and Tssk6 KO mice suggest their involvement in distinct subtypes of asthenozoospermia. Overall, this study elucidates how filamentous and enzymatic DMT proteins govern sperm function through divergent mechanisms, which have implications for molecular diagnosis of male infertility.