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Somatic cells accumulate genomic alterations with age; however, our understanding of mitochondrial DNA (mtDNA) mosaicism remains limited. Here we investigated the genomes of 2,096 clones derived from three cell types across 31 donors, identifying 6,451 mtDNA variants with heteroplasmy levels of ≳0.3%. While the majority of these variants were unique to individual clones, suggesting stochastic acquisition with age, 409 variants (6%) were shared across multiple embryonic lineages, indicating their origin from heteroplasmy in fertilized eggs. The mutational spectrum exhibited replication-strand bias, implicating mtDNA replication as a major mutational process. We evaluated the mtDNA mutation rate (5.0 × 10 −8 per base pair) and a turnover frequency of 10–20 per year, which are fundamental components shaping the landscape of mtDNA mosaicism over a lifetime. The expansion of mtDNA-truncating mutations toward homoplasmy was substantially suppressed. Our findings provide comprehensive insights into the origins, dynamics and functional consequences of mtDNA mosaicism in human somatic cells. Analysis of 2,096 single-cell clones from three tissues of 31 healthy donors characterizes mitochondrial DNA mosaicism and highlights the following two origins of mtDNA variants: heteroplasmy in the fertilized egg and postzygotic mutations.

As we age, our cells acquire DNA mutations, resulting in cell-to-cell genomic heterogeneity. We characterized the landscape of mitochondrial DNA (mtDNA) heterogeneity in healthy human cells. Our observations provide deeper insight into the frequency of new mitochondrial mutations and the mechanisms that propagate low-level mutations in mtDNA over a lifetime.

The direct collocation method transforms a trajectory optimization problem into a nonlinear programming (NLP) problem by discretizing both control and state variables. During the NLP solution process, repeated calculations of the first and second derivatives of the NLP and the values of the dynamic system at each discrete point are required, leading to great computational complexities. Therefore, this paper proposes the following method: First, the hyper-dual number method is introduced to accurately identify the sparsity of the second-derivative matrix of the NLP and to determine the locations of the non-zero elements. Then, a multi-core parallel approach is used to rapidly compute the non-zero elements of the first and second derivatives of the NLP as well as the values of the dynamic system at each discrete point. Finally, OpenMP is employed for programming calculation in the C++ environment to further enhance computational efficiency from the perspective of programming language. Simulation results demonst

Bright and fast scintillators are highly crucial for high-speed X-ray imaging in the medical diagnostic radiology including angiography and cardiac computed tomography. The CsPbBr 3 nanocrystal scintillator featuring a nanosecond radioluminescence decay time is a promising candidate. However, it suffers from a substantial photon self-absorption limiting the light output, and being bright and fast simultaneously is difficult. Here we design and in-situ synthesize multi-site ZnS(Ag)-CsPbBr 3 heterostructures to modulate the bright and fast features of scintillators. We find external energy from ZnS(Ag) can effectively transfer to CsPbBr 3 based on the non-radiative Förster resonance energy transfer, resulting in a light yield of 40,000 photons MeV −1 . By combing a radioluminescence decay time of 36 ns and a spatial resolution of 30 lp mm −1 , the scintillator enables high-speed X-ray imaging at 200 frames per second. This study showcases the structure design is significant for obtaining bright and fast perovskite scintillators for the real-time X-ray imaging. Yang et al. report in-situ growth of ZnS(Ag)-CsPbBr3 heterostructures through all solid-phase synthesis for X-ray scintillators. The multiple contact sites promote light yield via efficient energy transfer from ZnS(Ag) into CsPbBr3 and enable fast decay for high-speed X-ray imaging at 200 fps.

In this paper, we propose a robust deep learning (DL)-based method for Direction-of-Arrival (DOA) estimation. Specifically, we develop a novel CRDCNN-LSTM network architecture, which integrates a Cross-Residual Depthwise Convolutional Neural Network (CRDCNN) with a Long Short-Term Memory (LSTM) module for effective capture of both spatial and temporal features. The CRDCNN employs multi-level cross-residual connections and depthwise separable convolutions to enhance feature diversity while mitigating issues such as gradient vanishing and overfitting. Furthermore, a customized FD loss function, combining Focal Loss and Dice Loss, is introduced to emphasize low-confidence samples and promote sparsity in the spatial spectrum, thereby improving the precision and overall effectiveness of DOA estimation. A post-processing strategy based on peak detection and quadratic interpolation is also employed to refine DOA estimations and reduce quantization errors. Simulation results demonstrate that the proposed approach achieves significantly higher estimation accuracy and resolution than conventional methods and current DL models under varying SNR and snapshot conditions. In addition, it offers distinct advantages in terms of generalization and computational efficiency.

The operation of traditional aqueous-electrolyte zinc-ion batteries is adversely affected by the uncontrollable growth of zinc dendrites and the occurrence of side reactions. These problems can be avoided by the development of functional hydrogel electrolytes as replacements for aqueous electrolytes. However, the mechanism by which most hydrogel electrolytes inhibit the growth of zinc dendrites on a zinc anode has not been investigated in detail, and there is a lack of a large-scale recovery method for mainstream hydrogel electrolytes. In this paper, we describe the development of a recyclable and biodegradable hydrogel electrolyte based on natural biomaterials, namely chitosan and polyaspartic acid. The distinctive adsorptivity and inducibility of chitosan and polyaspartic acid in the hydrogel electrolyte triggers a double coupling network and an associated synergistic inhibition mechanism, thereby effectively inhibiting the side reactions on the zinc anode. In addition, this hydrogel electrolyte played a crucial role in an aqueous acid-based Zinc/MnO 2 battery, by maintaining its interior two-electron redox reaction and inhibiting the formation of zinc dendrites. Furthermore, the sustainable biomass-based hydrogel electrolyte is biodegradable, and could be recovered from the Zinc/MnO 2 battery for subsequent recycling. Functional hydrogel electrolytes show promising potential for enhancing the sustainability of aqueous zinc-ion batteries. Here, the authors introduce a biomass-based hydrogel electrolyte that not only prevents side reactions on the zinc anode but also enables easy retrieval from the zinc batteries.

Wall shear stress is an important quantity in fluid mechanics, but its measurement is a challenging task. An approach to measure wall shear stress vector distribution using shear-sensitive liquid crystal coating (SSLCC) is described. The wall shear stress distribution on the test surface beneath high speed jet flow is measured while using the proposed technique. The flow structures inside the jet flow are captured and the results agree well with the streakline pattern that was visualized using the oil-flow technique. In addition, the shock diamonds inside the supersonic jet flow are visualized clearly using SSLCC and the results are compared with the velocity contour that was measured using the particle image velocimetry (PIV) technique. The work of this paper demonstrates the application of SSLCC in the measurement/visualization of wall shear stress in high speed flow.

For the trajectory planning problem of hypersonic glide vehicles, an adaptive mesh-refined sequential convex optimization algorithm is proposed to overcome oscillation and non-convergence issues in existing methods, effectively reducing iterations and computation time. An adaptive mesh refinement strategy is integrated into the algorithm to further enhance trajectory accuracy and efficiency. First, the nonlinear problem is transformed into convex subproblems through linearization, discretization, and the introduction of slack variables, penalty functions, and trust regions. Second, an adaptive mesh refinement method is incorporated into the algorithm. Finally, taking the trajectory optimization problem of a hypersonic glide vehicle under complex constraints as an example, randomly generated initial control profiles are solved and integrated to evaluate accuracy. Results show that the proposed algorithm achieves faster computation, better convergence, and stronger robustness. Moreover, the adaptive mesh strategy significantly improves solution accuracy with only minor increases in iterations and computation time.

Artificial intelligence has slowly become the focus of research in various countries, and the application of artificial intelligence has become more and more extensive, and it can be seen in more and more industries. However, the application of artificial intelligence technology to the sports industry is still an attempt with unpredictable results, but it will be a bold attempt. In addition, in the process of the integration and development of artificial intelligence and the sports industry, there must be some problems. This requires researchers and staff to have enough patience to solve these problems, and through joint efforts to continuously improve, make artificial intelligence and sports the integrated development of the industry is more stable. This paper adopts a combination of empirical analysis and theoretical analysis, systematically researches the current status of the development of the sports industry with artificial intelligence, and analyzes the development trend of this new type of sports industry. The results of the experiment show that the market size of China's artificial intelligence sports education and the growth rate of the number of users have both remained above 20%, which has doubled in just four years, showing the rapid development of it. It proves from the side that the unique environmental advantages and sufficient talent advantages of the artificial intelligence sports industry have laid a strong human resource foundation for the sports industry in the central plains urban agglomeration; the artificial intelligence sports industry has a wealth of traditional characteristic sports industry projects, and is the development of the artificial intelligence sports industry provides a rich resource base for the sports industry.

This paper investigates the impact of incorporating trim deflection constraints in missile trajectory optimization, aiming to address the inaccuracies that may arise from neglecting such constraints in traditional methods. Based on the dynamics and aerodynamic models of the AM120D air-to-air missile, a trajectory optimization problem that includes trim deflection constraints is formulated and solved using the Radau pseudospectral method. The results indicate that incorporating trim deflection constraints leads to significant changes in flight time, maximum altitude, and maximum range, with noticeable differences in the trends of angle of attack, Mach number, and flight path angle during flight. The variation in key parameters ranges from a minimum of 12.7% to a maximum of 26.9%, highlighting substantial discrepancies between trajectories with and without trim deflection constraints. Further analysis reveals that the influence of different initial center of mass positions on the trajectory is relatively small, while the impact of trim deflection constraints on the optimization results is significant. This underscores the critical importance of incorporating trim deflection constraints in trajectory optimization research, as it ensures that the results are more realistic and valuable for engineering applications.