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De novo design of miniproteins targeting GPCRs
Dynamic weight and sensitive factor-based geological suitability evaluation of underground space development in Yangtze delta’s Hangjiahu plain
Designing quantum chemistry algorithms with just-in-time compilation
We introduce just-in-time (JIT) compilation to the integral kernels for Gaussian-type orbitals to enhance the efficiency of electron repulsion integral computations. For Coulomb and exchange (JK) matrices, JIT-based algorithms yield a 2× speedup for the small 6-31G* basis set over GPU4PySCF v1.4 on an NVIDIA A100-80G GPU. By incorporating a novel algorithm designed for orbitals with high angular momentum, the efficiency of JK evaluations with the large def2-TZVPP basis set is improved by up to 4×. The core CUDA implementation is compact, comprising only ∼1000 lines of code, including support for single-precision arithmetic. Furthermore, the single-precision implementation achieves a 3× speedup over the previous state-of-the-art.
Should I get a dog? What to know about pet ownership as a scientist
A V2X communication resource allocation method based on graph neural networks and deep reinforcement learning
The influence of a type I antifreeze protein and its mutants on methane hydrate adsorption-inhibition: A molecular dynamics simulation study
The formation of methane hydrates in oil and gas pipelines poses significant challenges to flow assurance, driving the urgent need for efficient and environmentally friendly kinetic hydrate inhibitors. The moderately active type I winter flounder antifreeze protein (wfAFP) exerts its ability to inhibit methane hydrate growth by binding to methane hydrate cages via specific amino acids [Thr-(i), Ala-(i+4), Ala-(i+7), where i = 2, 13, and 24]. However, the design strategy to enhance its methane hydrate growth inhibitory activity remains incompletely elucidated. In this study, we designed modifications to improve wfAFP’s methane hydrate growth inhibitory capacity based on key positions. The results demonstrate that the simultaneous mutation of Ala to Thr at the Ala-(i+4) and Ala-(i+7) positions (i = 2, 13, and 24) significantly enhances its ability to inhibit methane hydrate growth. Further analysis reveals that this system exhibits the lowest methane hydrate content and forms the highest number of hydrogen bonds with surrounding water molecules (with the longest lifetime), and the entire protein adopts a stable adsorption conformation parallel to the methane hydrate surface. The hydroxyl groups of threonine residues maintain the hydrogen bond network during binding to methane hydrates, while the methyl groups stabilize the hydrophobic embedding structure within hydrate cages. These findings provide a method for enhancing the methane hydrate growth inhibitory activity of moderately active AFPs and offer theoretical support for designing efficient and environmentally friendly hydrate inhibitors.
Ebola outbreak: the data that show why researchers are so alarmed
Lysosome-related biomarkers in peripheral blood immune cells discriminate sepsis from SIRS
A scalable diagonalization framework for tensor-product bitstring selected configuration interaction
Selected configuration interaction (SCI) methods are effective for treating strongly correlated electronic systems, yet their scalability has long been limited by implementations that replicate the configuration interaction (CI) vector across processes, leading to severe memory bottlenecks. Here, we present a fully distributed diagonalization framework tailored for extremely large selected determinant spaces, directly addressing this major scalability bottleneck of modern SCI methods. The method is grounded in a tensor-product bitstring (TPB) representation, in which determinants are organized through a TPB structure constructed from selected α- and β-bitstrings, and is referred to as tensor-product bitstring SCI (TBSCI). An efficient TBSCI eigensolver is developed based on a novel bitstring-based Hamiltonian evaluation algorithm together with a suite of MPI communication strategies designed to improve parallel efficiency. Large-scale full configuration interaction (FCI) benchmarks, employed as communication-intensive stress tests, demonstrate that the implemented TBSCI eigensolver continues to reduce the wall time for distributed diagonalization of 2.6 × 1012 determinants, reaching 54 000 nodes (more than 2.5 × 106 cores) on supercomputer Fugaku. Beyond scalability, we investigate the structural compactness of the TPB representation and show that selecting α- and β-bitstrings according to their collective weights in a reference SCI wavefunction yields TPB-based wavefunctions approaching the FCI limit while using only a small fraction of determinants. These results establish TBSCI as a scalable SCI methodology and provide evidence for the intrinsic compactness of the TPB representation.
In vivo assessment of collagen transdermal absorption in murine and human skin using photoacoustics microscopy
Abstract The therapeutic potential of collagen for transdermal applications has been hindered by its limited penetration efficiency due to its high molecular weight. This study systematically evaluates the transdermal behavior of C3-modified collagen (ICG-C3) in murine, rat, and human skin using photoacoustic imaging (PAI). Dynamic PAI monitoring revealed that ICG-C3 exhibited enhanced penetration (3-fold higher dermal signal intensity than free ICG, P < 0.05) and prolonged retention (a 181.6% longer half-life than unmodified collagen, P < 0.001) in animal models. Human trials further demonstrated that the efficacy of ICG-C3 was dependent on the anatomical site: it achieved a penetration depth of 210 ± 30 μm ( P < 0.01) in dorsal skin (characterized by a thick stratum corneum) and it exhibited a reduced signal decay rate to -25% of controls ( P < 0.05) in ventral skin (with high sweat gland density). Non-destructive PAI visualized the directional migration of ICG-C3 along skin appendages. Our findings validate the cross-species universality of C3 modification and establish PAI as a robust tool for real-time transdermal analysis, offering critical insights for developing collagen-based therapeutics and advancing photoacoustic imaging in biomedical research.
Guest Editorial: David Jonas Festschrift
The David Jonas Festschrift is a collection of 67 contributions that cover the latest developments and applications in multidimensional and ultrafast spectroscopies, spanning a wide range of topics. The editorial provides background and summarizes the contributions to the special issue across the areas of experimental advances, theory and computation, proteins and biological systems, nanomaterials, molecules, liquids, and soft matter.
Thermal-structural optimization and experimental validation of modified pyramid solar stills using finite element analysis
Atomic-scale structural inversion of interfacial water from atomic force microscopy
The atomic-scale structure of interfacial water plays a central role in electrochemistry, catalysis, friction, and biological engineering. Although atomic force microscopy (AFM) provides high spatial resolution, direct determination of atomic water structures remains challenging due to weak hydrogen contrast and the complex relationship between AFM images and underlying atomic configurations. Here, we develop a closed-loop, physics-informed structural inversion framework for interfacial water from multi-height AFM images. This framework combines conditional generative adversarial learning with an explicit and interpretable structural descriptor that explicitly encodes atomic positions and hydrogen orientations, establishing a direct link between AFM contrast and atomic configuration. Trained on simulated AFM data, the method achieves high accuracy in localizing atomic positions and determining hydrogen orientation. For experimental AFM images, automated preprocessing and structure-aware postprocessing procedures yield physically plausible atomic structures that reproduce the observed AFM contrast after relaxation, despite experimental noise and limited height sampling. Rather than targeting a unique solution, this approach provides a robust initialization for AFM inverse problems, substantially reducing the configurational search space and offering a general strategy applicable to other hydrogen-rich and weakly bonded interfacial systems.
Autonomous closed-loop framework for reproducible perovskite solar cells
An improved attention guided convolutional neural network and transformer hybrid model for emotion classification in traditional Chinese paintings
Performances of experts and automated methods on new multiple sclerosis lesions detection: insights from the MSSeg2 challenge
Kernel dynamic orthonormal subspace analysis for monitoring hybrid electric vehicle powertrain faults
Abstract Hybrid electric vehicle (HEV) powertrain systems exhibit complex dynamic and nonlinear characteristics due to the coupling effects among mechanical, electrical, and thermal subsystems. Traditional multivariate statistical process monitoring (MSPM) methods based on the time lag shift method (TLSM) may suffer from redundant information problems where historical data not used for prediction can contaminate the extracted features and reduce fault detection sensitivity. To address this limitation, this paper proposes a kernel dynamic orthonormal subspace analysis (KDOSA) method for monitoring HEV powertrain faults. The proposed method extends the OSA framework to the kernel feature space using Gaussian kernel functions, aiming to capture nonlinear dependencies while maintaining orthogonal separation between dynamic and static components. By decomposing real-time data into dynamic and static subspaces in the reproducing kernel Hilbert space, KDOSA is designed to mitigate the redundant information problem inherent in TLSM-based kernel methods such as dynamic kernel PCA. A comprehensive monitoring framework is developed with $$T^2$$ indices for both dynamic and static subspaces, providing fault detection capability and diagnostic information about fault origins. The effectiveness of the proposed method is examined through numerical simulations and real-world HEV powertrain experiments. Experimental results demonstrate that KDOSA achieves favorable fault detection performance with performance index (PI) values exceeding 95% across all test scenarios without triggering false alarms in the tested cases, showing improved performance compared with existing OSA-based nonlinear dynamic methods.