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MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells
Abstract The cardiac ryanodine receptor (RyR2) constitutes the molecular basis of the process of calcium-induced calcium release where activation of RyR2s can be locally regenerative. Here, we present purely optical data of RyR2 distribution with sub-molecular resolution by applying 3D MINFLUX microscopy. Using single-domain antibodies and DNA-PAINT we determine the location of individual RyR2 subunits with high precision (~3 nm) and resolve the 3D orientations of RyR2s in-situ. We measured labeling efficiencies of ~50%, implying RyR2 tetramer detection probability approaching 95%. In HEK293 cells, RyR2 expression was dense, with some clusters containing several hundred RyR2s. Ventricular myocytes from mice contained large clusters containing many tens of close-packed RyR2s, resolving apparent discrepancies between electron microscopy and previous super-resolution microscopy data. The methodology developed here reveals the full 3D morphological complexity of RyR2 channels and is applicable to other multi-subunit complexes in a variety of cell types.
Global prevalence of occult hepatitis B virus infection in HBcAb-positive individuals: a systematic review and meta-analysis
Multi-channel kinetics of fibrin network self-assembly
Fibrin networks are responsible for blood clotting and are widely used in tissue engineering and numerous biomedical applications. We developed a coarse-grained kinetic theory of self-assembly of fibrin networks from homogeneous solutions of fibrinogen and thrombin. Thrombin converts fibrinogen into fibrin monomers. The polymerization of these monomers results in the formation of intermediate products, protofibrils. Fibrin fibers form as a result of the diffusion-controlled aggregation of protofibrils. We considered two competing channels of fiber growth: the attachment of protofibrils to the ends of fibers, leading to fiber elongation, and lateral aggregation, increasing the diameter of fibers. The presented diagram of network self-assembly shows two main regimes. In a single-stage, thrombin-controlled regime, the protofibrils are immediately deposited onto network fibers. In a two-stage, kinetically controlled regime, network fibers form from a pre-existing solution of protofibrils. We determined the analytical dependencies of the gel formation rate and network structure parameters on fibrinogen and thrombin concentrations and reaction rates. These results are consistent with the experimental data obtained under physiological conditions, as well as at various ionic strengths and pH levels.
Quantifying the bystander effect of antimicrobial use on the gut microbiome and resistome in Malawian adults
Prediction of bearing capacity of ring footings on cohesive frictional soils using Terzaghi stability factors and Kolmogorov Arnold networks
Nonadiabatic dynamics of photoexcited thiopyridone isomers: An interplay between El-Sayed’s conditions and energy gap law
We investigated the non-adiabatic dynamics of photoexcited thiopyridone systems across their ortho-, meta-, and para-isomeric forms. The relaxation pathways of the three isomers in both gas phase and solvent environments are mapped using surface hopping dynamics based on time-dependent density functional theory. Our analysis highlights the influence of isomeric structures on photophysical behavior, offering insights into design principles to control photochemical phenomena. The simulations suggest a systematic reduction in the rate of intersystem crossing (ISC) from ortho- to meta- to para-isomer. Comparisons with multiconfigurational wave function methods in the gas phase further demonstrate how electronic structure influences the predicted dynamical pathways. The simulated dynamics demonstrates that the spin–orbit coupling strength alone does not determine the rate of ISC, as both state energetics and underlying electronic and structural features play decisive roles. These aspects explain the much slower ISC in the para-isomer, as well as the non-negligible role of the El-Sayed forbidden pathway in the computed ISC dynamics.
Discovery of high-temperature charge order and time-reversal symmetry-breaking in the kagome superconductor YRu3Si2
Information theory and thermodynamic study of a 2D harmonic oscillator modified by inverse-square potential
Quasiparticle energies and electron density reorganization in B- and N-doped carbon quantum dots: A GW study
Understanding the electronic properties of quantum dots (QDs) is essential for controlling their spectroscopic and transport behavior. Here, we investigate how boron and nitrogen doping affect electron-density reorganization in pyrene-derived QD models, including both substitutional doping and surface functionalization. The analysis combines information-theoretic descriptors, namely Shannon entropy, Fisher information, and Kullback–Leibler information gain, with quasiparticle energy levels obtained from GW calculations. Boron doping produces quasi-degenerate ionization energies together with positive first and second electron affinities, whereas nitrogen doping leads to a pronounced reduction of ionization energies and very small or even negative electron affinities. These results demonstrate that distinct dopants induce characteristic patterns in both electronic density redistribution and quasiparticle energies. The combined use of information-theoretic descriptors with GW calculations provides a rigorous framework for quantifying dopant-dependent modifications of the electronic structure in QDs.
Staphylococcus aureus fatty acid metabolism governs saeRS-mediated aggregation in joint infections
Enhancing large particle recovery in high-throughput functional cell sorting through ΔBOP optimization
Toward a parallel, quadrature-based second-order algebraic diagrammatic construction method for electronic excitations: Møller–Plesset ground state and first order
Large-scale applications of wavefunction-based excited-state methods are hindered by the enormous storage demands of electron repulsion integrals (ERIs) and by the computational scaling of Hamiltonian matrix element evaluations. At the same time, conventional formulations of these methods are not well suited for parallelization due to complex coupling patterns inherent to the evaluated expressions. In this work, we present the reformulation of an algebraic diagrammatic construction (ADC) scheme using a seminumerical ERI decomposition approach together with the well-established Laplace transform technique. In contrast to previous studies, we maintain a molecular-orbital (MO) formulation, exploiting its intrinsically reduced dimensionality. With the goal of developing a quadrature-based second-order ADC [Q-ADC(2)] method, we here begin with a detailed investigation of computational aspects inherent to this novel methodology by applying it to full second-order Møller–Plesset (MP2) perturbation theory and to the first-order ADC [ADC(1)] scheme, thereby establishing the Q-MP2 and Q-ADC(1) methods. Compared to conventional implementations, both memory requirements and computational scaling are reduced by one order. We thoroughly investigate the accuracy of the new methods depending on the numerical integration grids used and compare different algorithmic variants arising from the MO-based formulation. Furthermore, the effects of the approximation on the ERI symmetry are discussed in detail. The potential of this novel methodology for large-scale applications is demonstrated by simulating UV–Vis absorption spectra of important fluorophores using up to 1978 basis functions. The ability to systematically balance accuracy and computational effort makes this quadrature-based approach particularly promising for extension to the ADC(2) scheme, which will be presented in a subsequent publication.
TMEM120A maintains adipose tissue lipid homeostasis through ER CoA channeling
Proximal guided hybrid federated learning approach with parameter efficient adaptive intelligence for pneumonia diagnosis
Abstract Pneumonia remains a serious worldwide health concern, particularly in low resource countries, where prompt diagnosis is challenging. Early detection relies on chest radiography, but data privacy rules and patient data fragmentation make AI model building difficult. Federated Learning allows collaborative model training without patient data sharing, a promising solution. Standard federated learning methods like FedAvg suffer with data heterogeneity and significant communication overhead. To overcome these constraints, this research proposes an upgraded federated framework with FedProx, which mitigates client drift in non-IID contexts by proximal optimization and Low-Rank Adaptation, a parameter-efficient fine-tuning technique that minimizes communication costs. Vision Transformers are used as the backbone architecture for chest X-ray categorization because they capture the global visual context better than convolutional models. The tiny memory footprint proposed in this research, fits resource-constrained medical infrastructure. The proposed technique was validated for a pneumonia classification job utilizing the publicly available Chest X-Ray Images dataset, which was distributed across simulated clients to replicate real-world healthcare organizations. The model’s performance is measured using accuracy, precision, recall, F1-score, AUC and system-level measures including communication cost per round and convergence rate. The proposed federated model had 88.5% classification accuracy under data heterogeneity and reduced communication overhead and computation cost. Explainability research employing attention heatmaps supports the model’s clinically important pulmonary areas, boosting clinical adoption, trust and transparency.
From database to prediction: Machine learning for 5-f elements coordination using actinide x-ray experimental spectra (AXES) collection
The Actinide X-ray Experimental Spectra (AXES) database has been presented as a comprehensive database of x-ray absorption spectroscopy (XAS) spectra. It is the largest database of experimental spectra of actinides with a diverse range of measurement techniques (standard resolution x-ray absorption near edge structure and high energy resolution fluorescence detected XAS), absorption edges (L3, M4, M5, and less common edges), and absorber types (Th, U, Np, Pu, and Am). The spectra have been aligned and normalized to facilitate further analysis, while the original unprocessed data have been retained for reference. Coordination information derived from the spectra and their corresponding structures has been integrated into a convolutional neural network to construct a structural property model capable of predicting the presence or absence of uranium atoms in various coordination environments. The model’s predictive accuracy and reliability can be further enhanced by expanding the AXES database or employing transfer learning techniques. In this study, the model has been pre-trained using Fe K-edge XAS spectra. In addition, key spectral regions critical for coordination number prediction have been identified using the Shapley Additive Explanations (SHAP) approach. The SHAP-value distribution indicates that spectral features associated with six-coordination uranium primarily appear in the edge and post-edge regions, while those linked to eight-coordination uranium predominantly influence only the edge shape. This analysis underscores the model's potential for advancing actinide coordination studies.
Coordination of chromosome segregation and cell division in the archaeon Sulfolobus acidocaldarius
Salivary short chain fatty acids serve as biomarkers of periodontal inflammatory burden
An effective bath state approach to model infrared spectroscopy and intramolecular dynamics in complex molecules
When a molecule contains more than a few atoms, its full-dimensional dynamics becomes untractable, especially when introducing temperature effects. In such cases, it can be interesting to focus only on a few degrees of freedom and model the rest of the molecule as a finite-dimensional bath. In this prospect, we extend the effective bath state (EBS) method that we had first developed and benchmarked in Attal et al. [J. Chem. Phys. 160, 044107 (2024)] to describe the spectroscopy and intramolecular dynamics of complex isolated molecules. The EBS method is a system–bath approach based on the coarse-graining of the bath into a reduced set of effective energy states. It allows for a significant reduction of the bath dimension and makes finite-temperature calculations more accessible. In order to treat a realistic molecule, the method is extended to include polynomial couplings in the bath coordinates. The ability of the method to model temperature-resolved infrared spectra and to follow population transfers between the vibrational modes of the molecule is first tested on a ten-mode model system. The extended method is then applied to the realistic case of phenylacetylene.
Integrating protein language and geometric deep learning models for enhanced vaccine antigen prediction
Abstract Vaccines are the most effective tool in preventing and managing infectious diseases. One of the critical challenges in vaccine development is the selection of suitable target antigens from the thousands of proteins produced by pathogens. Artificial intelligence is anticipated to play a significant role in addressing this challenge. In this study, we develop a framework termed PLGDL for protective antigen prediction that employs Protein Language and Geometric Deep Learning models. This framework leverages both primary sequence features and three-dimensional structural features of protein antigens, thereby reducing the biases associated with manually curated features. Our integrated model exhibits robustness across both constructed and public datasets and is applicable to viruses, bacteria, and eukaryotic pathogens. Notably, when applied to the ongoing Mpox outbreak, our model not only quickly identifies multiple known antigens but also discovers a protective antigen: G10R. Here, our study provides a high-performance screening tool for protective vaccine antigen prediction by synergistically utilizing the capabilities of protein language and geometric deep learning models, providing substantive insights and methodological advancements for rapid vaccine development.