The diagnostic value of blood metagenomic next-generation sequencing in patients with acute hematogenous osteomyelitis

AimsThis study aims to evaluate the diagnostic value of blood metagenomic next-generation sequencing (mNGS) in detecting pathogens from patients clinically diagnosed as acute hematogenous osteomyelitis (AHO).MethodsThis retrospective study enrolled 66 patients with AHO. The test results of mNGS and bacterial culture on different samples, including blood and puncture fluid samples, from patients with AHO were compared to explore the diagnostic value of blood mNGS. Besides, this study also explored the efficacy of blood mNGS in decision making for antibiotic administration and analyzed the factors associated with the positive result of blood mNGS.ResultsThe most common causative pathogens were Staphylococcus and Streptococcus. The sensitivity of blood mNGS (77.3%) was higher than that of blood culture (42.4%) (P<0.001), while the turnaround time of blood mNGS (2.1 ± 0.4 d) is much less than that of blood culture (6.0 ± 2.1 d) (P<0.001). Besides, the sensitivity of blood mNGS tests (77.3%) was slightly lower than that of puncture fluid mNGS (89.4%). Furthermore, detection comparison at pathogen level unravels that blood mNGS might be suitable for diagnosing AHO caused by common pathogens, while puncture fluid mNGS could be considered as preferred examination in diagnosing AHO caused by uncommon pathogens. Finally, three independent factors associated with the true positive result of blood mNGS in patients with AHO were identified, including Gram-positive pathogens (OR=24.4, 95% CI = 1.4-421.0 for Staphylococcus; OR=14.9, 95%CI= 1.6-136.1 for other Gram-positive bacteria), body temperature at sampling time (OR=8.2, 95% CI = 0.6-107.3 for body temperature of >38.5°C; OR=17.2, 95% CI = 2.0-149.1 for patients who were chilling), and no use of antibiotics before sampling (OR=8.9, 95% CI =1.4-59.0).ConclusionThis is the first report on evaluating and emphasizing the importance of blood mNGS in diagnosing AHO. Blood sample might be an alternative sample for puncture fluid for mNGS, and its extensive application in diagnosing AHO could be expected

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

A Multi-Population Mean-Field Game Approach for Large-Scale Agents Cooperative Attack-Defense Evolution in High-Dimensional Environments

As one of the important issues of multi-agent collaboration, the large-scale agents’ cooperative attack–defense evolution requires a large number of agents to make stress-effective strategies to achieve their goals in complex environments. Multi-agent attack and defense in high-dimensional environments (3D obstacle scenarios) present the challenge of being able to accurately control high-dimensional state quantities. Moreover, the large scale makes the dynamic interactions in the attack and defense problems increase dramatically, which, using traditional optimal control techniques, can cause a dimensional explosion. How to model and solve the cooperative attack–defense evolution problem of large-scale agents in high-dimensional environments have become a challenge. We jointly considered energy consumption, inter-group attack and defense, intra-group collision avoidance, and obstacle avoidance in their cost functions. Meanwhile, the high-dimensional state dynamics were used to describe the motion of agents under environmental interference. Then, we formulated the cooperative attack–defense evolution of large-scale agents in high-dimensional environments as a multi-population high-dimensional stochastic mean-field game (MPHD-MFG), which significantly reduced the communication frequency and computational complexity. We tractably solved the MPHD-MFG with a generative-adversarial-network (GAN)-based method using the MFGs’ underlying variational primal–dual structure. Based on our approach, we carried out an integrative experiment in which we analytically showed the fast convergence of our cooperative attack–defense evolution algorithm by the convergence of the Hamilton–Jacobi–Bellman equation’s residual errors. The experiment also showed that a large number of drones can avoid obstacles and smoothly evolve their attack and defense behaviors while minimizing their energy consumption. In addition, the comparison with the baseline methods showed that our approach is advanced

A Multi-Population Mean-Field Game Approach for Large-Scale Agents Cooperative Attack-Defense Evolution in High-Dimensional Environments

As one of the important issues of multi-agent collaboration, the large-scale agents’ cooperative attack–defense evolution requires a large number of agents to make stress-effective strategies to achieve their goals in complex environments. Multi-agent attack and defense in high-dimensional environments (3D obstacle scenarios) present the challenge of being able to accurately control high-dimensional state quantities. Moreover, the large scale makes the dynamic interactions in the attack and defense problems increase dramatically, which, using traditional optimal control techniques, can cause a dimensional explosion. How to model and solve the cooperative attack–defense evolution problem of large-scale agents in high-dimensional environments have become a challenge. We jointly considered energy consumption, inter-group attack and defense, intra-group collision avoidance, and obstacle avoidance in their cost functions. Meanwhile, the high-dimensional state dynamics were used to describe the motion of agents under environmental interference. Then, we formulated the cooperative attack–defense evolution of large-scale agents in high-dimensional environments as a multi-population high-dimensional stochastic mean-field game (MPHD-MFG), which significantly reduced the communication frequency and computational complexity. We tractably solved the MPHD-MFG with a generative-adversarial-network (GAN)-based method using the MFGs’ underlying variational primal–dual structure. Based on our approach, we carried out an integrative experiment in which we analytically showed the fast convergence of our cooperative attack–defense evolution algorithm by the convergence of the Hamilton–Jacobi–Bellman equation’s residual errors. The experiment also showed that a large number of drones can avoid obstacles and smoothly evolve their attack and defense behaviors while minimizing their energy consumption. In addition, the comparison with the baseline methods showed that our approach is advanced

Selective Catalytic Hydrogenolysis of Carbon–Carbon σ Bonds in Primary Aliphatic Alcohols over Supported Metals

The selective scission of chemical bonds is always of great significance in organic chemistry. The cleavage of strong carbon–carbon σ bonds in the unstrained systems remains challenging. Here, we report the selective hydrogenolysis of carbon–carbon σ bonds in primary aliphatic alcohols catalyzed by supported metals under relatively mild conditions. In the case of 1-hexadecanol hydrogenolysis over Ru/TiO₂ as a model reaction system, the selective scission of carbon–carbon bonds over carbon–oxygen bonds is observed, resulting in <i>n</i>-pentadecane as the dominant product with a small quantity of <i>n</i>-hexadecane. Theoretical calculations reveal that the 1-hexadecanol hydrogenolysis on flat Ru (0001) undergoes two parallel pathways: i.e. carbon–carbon bond scission to produce <i>n</i>-pentadecane and carbon–oxygen bond scission to produce <i>n</i>-hexadecane. The removal of adsorbed CO on a flat Ru (0001) surface is a crucial step for the 1-hexadecanol hydrogenolysis. It contributes to the largest energy barrier in <i>n</i>-pentadecane production and also retards the rate for <i>n</i>-hexadecane production by covering the active Ru (0001) surface. The knowledge presented in this work has significance not just for a fundamental understanding of strong carbon–carbon σ bond scission but also for practical biomass conversion to fuels and chemical feedstocks