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Leading graph ordinary differential equation (ODE) models have offered generalized strategies to model interacting multi-agent dynamical systems in a data-driven approach. They typically consist of a temporal graph encoder to get the initial states and a neural ODE-based generative model to model the evolution of dynamical systems. However, existing methods have severe deficiencies in capacity and efficiency due to the failure to model high-order correlations in long-term temporal trends. To tackle this, in this paper, we propose a novel model named High-Order graPh ODE (HOPE) for learning from dynamic interaction data, which can be naturally represented as a graph. It first adopts a twin graph encoder to initialize the latent state representations of nodes and edges, which consists of two branches to capture spatio-temporal correlations in complementary manners. More importantly, our HOPE utilizes a second-order graph ODE function which models the dynamics for both nodes and edges in the latent space respectively, which enables efficient learning of long-term dependencies from complex dynamical systems. Experiment results on a variety of datasets demonstrate both the effectiveness and efficiency of our proposed method. 

This paper presents a stochastic three-dimensional focused transport simulation of solar energetic particles (SEPs) produced by a data-driven coronal mass ejection (CME) shock propagating through a data-driven model of coronal and heliospheric magnetic fields. The injection of SEPs at the CME shock is treated using diffusive shock acceleration of post-shock suprathermal solar wind ions. A time-backward stochastic simulation is employed to solve the transport equation to obtain the SEP time–intensity profile at any location, energy, and pitch angle. The model is applied to a SEP event on 2020 May 29, observed by STEREO-A close to ∼1 au and by Parker Solar Probe (PSP) when it was about 0.33 au away from the Sun. The SEP event was associated with a very slow CME with a plane-of-sky speed of 337 km s⁻¹at a height below 6R_Sas reported in the SOHO/LASCO CME catalog. We compute the time profiles of particle flux at PSP and STEREO-A locations, and estimate both the spectral index of the proton energy spectrum for energies between ∼2 and 16 MeV and the equivalent path length of the magnetic field lines experienced by the first arriving SEPs. We find that the simulation results are well correlated with observations. The SEP event could be explained by the acceleration of particles by a weak CME shock in the low solar corona that is not magnetically connected to the observers.

 

Abstract Long bone growth requires the precise control of chondrocyte maturation from proliferation to hypertrophy during endochondral ossification, but the bioenergetic program that ensures normal cartilage development is still largely elusive. We show that chondrocytes have unique glucose metabolism signatures in these stages, and they undergo bioenergetic reprogramming from glycolysis to oxidative phosphorylation during maturation, accompanied by an upregulation of the pentose phosphate pathway. Inhibition of either oxidative phosphorylation or the pentose phosphate pathway in murine chondrocytes and bone organ cultures impaired hypertrophic differentiation, suggesting that the appropriate balance of these pathways is required for cartilage development. Insulin-like growth factor 2 (IGF2) deficiency resulted in a profound increase in oxidative phosphorylation in hypertrophic chondrocytes, suggesting that IGF2 is required to prevent overactive glucose metabolism and maintain a proper balance of metabolic pathways. Our results thus provide critical evidence of preference for a bioenergetic pathway in different stages of chondrocytes and highlight its importance as a fundamental mechanism in skeletal development. 

Turbulence is ubiquitous in space plasmas. It is one of the most important subjects in heliospheric physics, as it plays a fundamental role in the solar wind—local interstellar medium interaction and in controlling energetic particle transport and acceleration processes. Understanding the properties of turbulence in various regions of the heliosphere with vastly different conditions can lead to answers to many unsolved questions opened up by observations of the magnetic field, plasma, pickup ions, energetic particles, radio and UV emissions, and so on. Several space missions have helped us gain preliminary knowledge on turbulence in the outer heliosphere and the very local interstellar medium. Among the past few missions, the Voyagers have paved the way for such investigations. This paper summarizes the open challenges and voices our support for the development of future missions dedicated to the study of turbulence throughout the heliosphere and beyond. 

Water-based coherent detection of broadband terahertz (THz) wave has been recently proposed with superior performances, which can alleviate the limited detection bandwidth and high probe laser energy requirement in the solid- and air-based detection schemes, respectively. Here, we demonstrate that the water-based detection method can be extended to the aqueous salt solutions and the sensitivity can be significantly enhanced. The THz coherent detection signal intensity scales linearly with the third-order nonlinear susceptibilityχ⁽³⁾or quadratically with the linear refractive indexη₀of the aqueous salt solutions, while the incoherent detection signal intensity scales quadratically withχ⁽³⁾or quartically withη₀, proving the underlying mechanism is the four-wave mixing. Both the coherent and incoherent detection signal intensities appear positive correlation with the solution concentration. These results imply that the liquid-based THz detection scheme could provide a new technique to measureχ⁽³⁾and further investigate the physicochemical properties in the THz band for various liquids.

 

Abstract Nonthermal, pickup ions (PUIs) represent an energetic component of the solar wind (SW). While a number of theoretical models have been proposed to describe the PUI flow, of major importance are in situ measurements providing us with the vital source of model validation. The Solar Wind Ion Composition Spectrometer (SWICS) instrument on board the Ulysses spacecraft was specifically designed for this purpose. Zhang et al. proposed a new, accurate method for the derivation of ion velocity distribution function in the SW frame on the basis of count rates collected by SWICS. We calculate the moments of these distribution functions for protons (H + ) and He + ions along the Ulysses trajectory for a period of 2 months including the Halloween 2003 solar storm. This gives us the time distributions of PUI density and temperature. We compare these with the results obtained earlier for the same interval of time, in which the ion spectra are converted to the SW frame using the narrow-beam approximation. Substantial differences are identified, which are of importance for the interpretation of PUI distributions in the 3D, time-dependent heliosphere. We also choose one of the shocks crossed by Ulysses during this time interval and analyze the distribution functions and PUI bulk properties in front of and behind it. The results are compared with the test-particle calculations and diffusive shock acceleration theory. 

In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset. 

Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods.