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DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials 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, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
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Iron homeostasis is crucial for optimal cardiac function. Iron deficiency and overload have been linked to the development of cardiomyopathy and heart failure (HF) via intricate mechanisms. Although the crucial role of SLC40A1 in iron metabolism by facilitating the efflux of cellular iron has been confirmed, its specific molecular functions in cardiovascular diseases remain poorly understood. In this study, we generated mice with inducible cardiomyocyte-specific overexpression of SLC40A1 for the first time. The overexpression of SLC40A1 in the cardiomyocytes of adult mice resulted in significant iron deficiency, leading to mitochondrial dysfunction, oxidative stress, and apoptosis, subsequently resulting in the development of fatal HF. Notably, SLC40A1 upregulation was observed in the ischemic region during the initial phase of myocardial infarction (MI), contributing to iron loss in the cardiomyocytes. Conversely, the cardiomyocyte-specific knockdown of SLC40A1 improved cardiac dysfunction after MI by enhancing mitochondrial function, suppressing oxidative stress, and reducing cardiomyocytes apoptosis. Mechanistically, Steap4 interacted with SLC40A1, facilitating SLC40A1-mediated iron efflux from cardiomyocytes. In short, our study presents evidence for the involvement of SLC40A1 in the regulation of myocardial iron levels and the therapeutic benefits of cardiomyocyte-specific knockdown of SLC40A1 in MI in mice.
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Insuficiencia Cardíaca , Deficiencias de Hierro , Enfermedades Mitocondriales , Infarto del Miocardio , Animales , Ratones , Apoptosis/genética , Insuficiencia Cardíaca/genética , Insuficiencia Cardíaca/metabolismo , Hierro/metabolismo , Enfermedades Mitocondriales/metabolismo , Infarto del Miocardio/metabolismo , Miocardio/metabolismo , Miocitos Cardíacos/metabolismo , Estrés Oxidativo/genéticaRESUMEN
Background: The clinical features and therapeutic measures of vestibular schwannoma (VS) radiation-related aneurysm (RRA) have not been well described. We reported the first VS RRA case admitted for acute anterior inferior cerebella artery (AICA) ischemic symptoms. Literature was reviewed to present the research fruits about VS RRAs, and some therapeutic advices were given. Materials and methods: A 54-year-old woman who had undergone GKS 10 years previously for a right VS was admitted to our hospital in 2018 because of sudden onset of severe vertigo and vomiting, accompanied with unsteady gait. During tumor resection, a dissecting aneurysm arose from the main trunk of AICA was encountered accidently within the tumor. The aneurysm was successfully treated with direct clip ligation, sparing the parent vessel. Data about this case were combined with those of other 11 radiation-related AICA aneurysm cases retrieved from the current literature. The following parameters were evaluated: Age, Sex, Diagnostic method, Location of aneurysm, Age of radiotherapy (Years)/Latency, Rupture, x-ray dosage, Type of radiotherapy, History of surgical resection of VS, Aneurysm Type, Morphology, Number, Treatment, Operative complications, Sequela, Outcome. VS RRAs mainly occurred in women (75%) with a median age of 62.5 years and were mainly located on AICA. Ruptured aneurysms accounted for 75.0% of the total cases. This paper reported the first VS case admitted with acute AICA ischemic symptoms. Cases with sacciform-like, irregular and fusiform-shaped aneurysms accounted for 50.0%, 25.0% and 25.0% of the total, respectively. After surgical treatment, 75.0% patients recovered, except for 3 patients who developed new ischemic consequence. Conclusion: Patients should be informed of the risk of RRAs after receiving radiotherapy for VS. In these patients, RRAs should be suspected when subarachnoid hemorrhage or AICA ischemic symptoms occurred. Active intervention should be conducted considering the high instability and bleeding rate of VS RRAs.
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Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning-based PES model. This scheme, we call DP Compress, is an efficient postprocessing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online.
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Aprendizaje Automático , Redes Neurales de la Computación , Simulación de Dinámica MolecularRESUMEN
Hantavirus infection is a global health challenge, causing widespread public concern. In recent years, cases of hantavirus infection in pregnant women have been reported in many countries. The infected pregnant women and their fetuses appear to have more severe clinical symptoms and worse clinical outcomes. Hence, to study the prevalence of hantavirus infection in pregnant women, this study will focus on the epidemiological distribution of the virus, different virus species penetrating the placental barrier, and factors affecting the incidence and clinical outcome of the infection in pregnant women and their fetuses. In addition, this review will also discuss the diagnostic tools and treatments for pregnant patients and provide an overview of the relevant future research.
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Infecciones por Hantavirus , Orthohantavirus , Femenino , Humanos , Embarazo , Análisis del Polimorfismo de Longitud de Fragmentos Amplificados , Cesárea , Infecciones por Hantavirus/diagnóstico , Infecciones por Hantavirus/epidemiología , Orthohantavirus/genética , PlacentaRESUMEN
We perform a systematic study on the structure and dynamics of warm dense aluminum (Al) at temperatures ranging from 0.5 to 5.0 eV with molecular dynamics utilizing both density functional theory (DFT) and the deep potential (DP) method. On one hand, unlike the Thomas-Fermi kinetic energy density functional (KEDF), we find that the orbital-free DFT method with the Wang-Teter non-local KEDF yields properties of warm dense Al that agree well with the Kohn-Sham DFT method, enabling accurate orbital-free DFT simulations of warm dense Al at relatively low temperatures. On the other hand, the DP method constructs a deep neural network that has a high accuracy in reproducing short- and long-ranged properties of warm dense Al when compared to the DFT methods. The DP method is orders of magnitudes faster than DFT and is well-suited for simulating large systems and long trajectories to yield accurate properties of warm dense Al. Our results suggest that the combination of DFT methods and the DP model is a powerful tool for accurately and efficiently simulating warm dense matter.