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Dynamic subcellular proteomics identifies regulators of adipocyte insulin action
Abstract Insulin acts on adipocytes to suppress lipolysis and increase glucose uptake to control whole-body glucose and lipid metabolism. Regulation of these processes by insulin signalling depends on changes in protein localisation. However, the extent of insulin-stimulated changes to the adipocyte spatial proteome, and the importance of these in the cellular insulin response, is unknown. Here, we use subcellular proteomics approaches to map acute insulin-stimulated protein relocalisation in adipocytes on a cell-wide scale. These data reveal extensive insulin-regulated protein redistribution, with hundreds of insulin-responsive proteins. These include the uncharacterised protein C3ORF18, which redistributes to the plasma membrane in response to insulin. Studies in C3ORF18-depleted adipocytes suggest this protein is required to maintain adipocyte insulin sensitivity. Overall, our data highlight the scale of protein relocalisation in the adipocyte insulin response, and provide an accessible resource to inform further studies into how changes in protein localisation contribute to cellular insulin responses.
Active phase separation triggered by chemotactic defects
When initial conditions are uniformly random, numerical simulations of Motility-Induced Phase Separation (MIPS) in active suspensions with average packing fractions above a specific (spinodal) threshold require relatively short run times. The question remains of how phase separation can be achieved within a feasible run time at lower packing fractions (binodal region). We demonstrate that defects functioning as attractive chemotactic centers can rapidly initiate the formation of local particle aggregates, which, under certain conditions, can detach and grow until phase separation is fully realized. Here, we provide a detailed investigation of defect-triggered MIPS in the binodal region. We suggest that this mechanism may facilitate the aggregation of bacteria into biofilms, with nutrient particles acting as chemotactic attractors.
Nanoporosity-driven deformation of additively manufactured nano-architected metals
Microwave and millimeter-wave spectroscopy of the potential interstellar carbonitrile cyclopropylacetonitrile
The pure rotational spectrum of cyclopropylacetonitrile has been investigated by jet-cooled impulse-excitation microwave spectroscopy and room-temperature chirped-pulse millimeter-wave spectroscopy. The spectra have been measured in the regions of 7–20 and 75–110 GHz, leading to the assignment of two trans and gauche conformations of this cyanoalkane. For both conformers, the spectral assignments were extended to all 13C and 15N mono-substituted isotopic species, allowing an accurate structural determination of the two conformers. Theoretical calculations [MP2/6-311++G(d,p), MP2/aug-cc-pVTZ, and B2PLYP/aug-cc-pVTZ] on the potential energy surface, electric properties, vibrational movements, and molecular structure complemented the experimental work. This comprehensive spectroscopic–computational study provides experimental rotational parameters of the ground and the lowest energy vibrational excited states, the 14N nuclear quadrupole coupling tensor, and the centrifugal distortion constants, permitting future radioastronomical investigations on the potential presence of this carbonitrile in the interstellar medium.
Physical echo state network based on the nonlinearity and dynamic response of ambipolar heterostructure transistors
Hydration and transport properties of cesium hydroxide and mixed cesium hydroxide–sodium nitrite aqueous solutions
This study explores the hydration and transport properties of aqueous cesium hydroxide (CsOH) solution, with or without 1 molar (M) sodium nitrite (NaNO2). Historic studies of electrolyte solutions indicate that Cs+ ions decrease viscosity and increase diffusion rates, whereas OH− ions have the opposite effect. Here, the influence of OH− was dominant in CsOH solutions, leading to increased viscosity and reduced diffusion rates. There was a linear relationship between diffusion coefficients and water activity, emphasizing the significant role of ion–water interactions in determining transport properties. This may be because the interaction between Cs+ and the anions is weak even when they are in direct contact with each other. The weakness of the ion-pairing was established through thermodynamic analysis. The findings suggest that ion-pairing is not the only important interaction controlling transport properties when ion-pairing is weak. Nonetheless, ion-pairing or obstructions did result in more sluggish transport properties as electrolyte concentrations increased. Overall, the research enhances the understanding of the complexities underlying ion interactions in multicomponent solutions.
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.