Effects of Ionizing Irradiation on Mouse Diaphragmatic Skeletal Muscle

Undesirable exposure of diaphragm to radiation during thoracic radiation therapy has not been fully considered over the past decades. Our study aims to examine the potential biological effects on diaphragm induced by radiation. One-time ionizing irradiation of 10 Gy was applied either to the diaphragmatic region of mice or to the cultured C2C12 myocytes. Each sample was then assayed for muscle function, oxidative stress, or cell viability on days 1, 3, 5, and 7 after irradiation. Our mouse model shows that radiation significantly reduced muscle function on the 5th and 7th days and increased reactive oxygen species (ROS) formation in the diaphragm tissue from days 3 to 7. Similarly, the myocytes exhibited markedly decreased viability and elevated oxidative stress from days 5 to 7 after radiation. These data together suggested that a single dose of 10-Gy radiation is sufficient to cause acute adverse effects on diaphragmatic muscle function, redox balance, and myocyte survival. Furthermore, using the collected data, we developed a physical model to formularize the correlation between diaphragmatic ROS release and time after irradiation, which can be used to predict the biological effects of radiation with a specific dosage. Our findings highlight the importance of developing protective strategies to attenuate oxidative stress and prevent diaphragm injury during radiotherapy

Calibration of the in-orbit center-of-mass of TaiJi-1

Taiji program is a space mission aiming to detect gravitational waves in the low frequency band. Taiji-1 is the first technology demonstration satellite of the Taiji Program in Space, with the gravitational reference sensor (GRS) serving as one of its key scientific payloads. For accurate accelerometer measurements, the test-mass center of the GRS must be positioned precisely at the center of gravity of the satellite to avoid measurement disturbances caused by angular acceleration and gradient. Due to installation and measurement errors, fuel consumption during in-flight phase, and other factors, the offset between the test-mass center and the center-of-mass (COM) of the satellite can be significant, degrading the measurement accuracy of the accelerometer. Therefore, the offset needs to be estimated and controlled within the required range by the center-of-mass adjustment mechanism during the satellite's lifetime. In this paper, we present a novel method, the Extended Kalman Filter combined with Rauch-Tung-Striebel Smoother, to estimate the offset, while utilizing the chi-square test to eliminate outliers. Additionally, the nonlinear Least Squares estimation algorithm is employed as a crosscheck to estimate the offset of COM. The two methods are shown to give consistent results, with the offset estimated to be $dx \approx$$-$$0.19$ mm, $dy \approx 0.64$ mm, and $dz \approx$$-$$0.82$ mm. The results indicate a significant improvement on the noise level of GRS after the COM calibration, which will be of great help for the future Taiji program.Comment: 8 pages, 9 figure

Oxidative Stress in Neurodegenerative Diseases: From Molecular Mechanisms to Clinical Applications

Increasing numbers of individuals, particularly the elderly, suffer from neurodegenerative disorders. These diseases are normally characterized by progressive loss of neuron cells and compromised motor or cognitive function. Previous studies have proposed that the overproduction of reactive oxygen species (ROS) may have complex roles in promoting the disease development. Research has shown that neuron cells are particularly vulnerable to oxidative damage due to their high polyunsaturated fatty acid content in membranes, high oxygen consumption, and weak antioxidant defense. However, the exact molecular pathogenesis of neurodegeneration related to the disturbance of redox balance remains unclear. Novel antioxidants have shown great potential in mediating disease phenotypes and could be an area of interest for further research. In this review, we provide an updated discussion on the roles of ROS in the pathological mechanisms of Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia, as well as a highlight on the antioxidant-based therapies for alleviating disease severity

Hypoxic Preconditioning Attenuates Reoxygenation-Induced Skeletal Muscle Dysfunction in Aged Pulmonary TNF-α Overexpressing Mice

Aim: Skeletal muscle subjected to hypoxia followed by reoxygenation is susceptible to injury and subsequent muscle function decline. This phenomenon can be observed in the diaphragm during strenuous exercise or in pulmonary diseases such as chronic obstructive pulmonary diseases (COPD). Previous studies have shown that PO2 cycling or hypoxic preconditioning (HPC), as it can also be referred to as, protects muscle function via mechanisms involving reactive oxygen species (ROS). However, this HPC protection has not been fully elucidated in aged pulmonary TNF-α overexpressing (Tg+) mice (a COPD-like model). We hypothesize that HPC can exert protection on the diaphragms of Tg+ mice during reoxygenation through pathways involving ROS/phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt)/extracellular signal regulated kinase (ERK), as well as the downstream activation of mitochondrial ATP-sensitive potassium channel (mitoKATP) and inhibition of mitochondrial permeability transition pore (mPTP).Methods: Isolated Tg+ diaphragm muscle strips were pre-treated with inhibitors for ROS, PI3K, Akt, ERK, or a combination of mitoKATP inhibitor and mPTP opener, respectively, prior to HPC. Another two groups of muscles were treated with either mitoKATP activator or mPTP inhibitor without HPC. Muscles were treated with 30-min hypoxia, followed by 15-min reoxygenation. Data were analyzed by multi-way ANOVA and expressed as means ± SE.Results: Muscle treated with HPC showed improved muscle function during reoxygenation (n = 5, p < 0.01). Inhibition of ROS, PI3K, Akt, or ERK abolished the protective effect of HPC. Simultaneous inhibition of mitoKATP and activation of mPTP also diminished HPC effects. By contrast, either the opening of mitoKATP channel or the closure of mPTP provided a similar protective effect to HPC by alleviating muscle function decline, suggesting that mitochondria play a role in HPC initiation (n = 5; p < 0.05).Conclusion: Hypoxic preconditioning may protect respiratory skeletal muscle function in Tg+ mice during reoxygenation through redox-sensitive signaling cascades and regulations of mitochondrial channels

Research on speed control of high-speed trains based on hybrid modeling

With the continuous improvement of train speed, the automatic driving of trains instead of driver driving has become the development direction of rail transit in order to realize traffic automation. The application of single modeling methods for speed control in the automatic operation of high-speed trains lacks exploration of the com-bination of train operation data information and physical model, resulting in low system modeling accuracy, which impacts the effectiveness of speed control and the operation of high-speed trains. To further increase the dynamic modeling accuracy of high-speed train operation and the high-speed train's speed control effect, a high-speed train speed control method based on hybrid modeling of mechanism and data drive is put forward. Firstly, a model of the high-speed train's mechanism was created by analyzing the train's dynamics. Secondly, the improved kernel-principal component regression algorithm was used to create a data-driven model using the actual opera-tion data of the CRH3 (China Railway High-speed 3) high-speed train from Huashan North Railway Station to Xi'an North Railway Station of "Zhengxi High-speed Railway," completing the mechanism model compensation and the error correction of the speed of the actual operation process of the high-speed train, and realizing the hybrid modeling of mechanism and data-driven. Finally, the prediction Fuzzy PID control algorithm was devel-oped based on the natural line and train characteristics to complete the train speed control simulation under the hybrid model and the mechanism model, respectively. In addition, analysis and comparison analysis were conduct-ed. The results indicate that, compared to the high-speed train speed control based on the mechanism model, the high-speed train speed control based on hybrid modeling is more accurate, with an average speed control error reduced by 69.42%. This can effectively reduce the speed control error, improve the speed control effect and oper-ation efficiency, and demonstrate the efficacy of the hybrid modeling and algorithm. The research results can provide a new ideal of multi-model fusion modeling for the dynamic modeling of high-speed train operation, further improve control objectives such as safety, comfort, and efficiency of high-speed train operation, and pro-vide a reference for automatic driving and intelligent driving of high-speed trains

SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases

Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools for modeling complex dynamics of multi-object physical systems. However, their generalization ability is limited by the inadequate consideration of physical inductive biases: (1) Existing studies overlook the continuity of transitions among system states, opting to employ several discrete transformation layers to learn the direct mapping between two adjacent states; (2) Most models only account for first-order velocity information, despite the fact that many physical systems are governed by second-order motion laws. To incorporate these inductive biases, we propose the Second-order Equivariant Graph Neural Ordinary Differential Equation (SEGNO). Specifically, we show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization. Additionally, we prove that the discrepancy between this learned trajectory of SEGNO and the true trajectory is bounded. Extensive experiments on complex dynamical systems including molecular dynamics and motion capture demonstrate that our model yields a significant improvement over the state-of-the-art baselines

Expression of ICOSL is associated with decreased survival in invasive breast cancer

Background Inducible co-stimulator (ICOS) is a CD28-related molecule exclusively expressed on activated T cells and plays a critical role in modulating the immune response in breast cancer. The blockage of ICOS pathway has been shown to inhibit the activity of Type 2 T helper cells, thus potentially protecting against cancer growth. The current study aims to investigate the correlation between inducible co-stimulator ligand (ICOSL) expression in tumor tissues and the prognoses of patients with invasive breast cancer. Methods Tumor samples from 562 Chinese patients with invasive breast carcinomas were collected between 2003 and 2010. The expression of ICOSL on breast tumor and adjacent non-cancerous tissue was determined via immunohistochemistry. The overall survival (OS) of patients with positive and negative ICOSL expression were described using Kaplan–Meier curves, respectively. Parametric correlation method was used to analyze the correlation between ICOSL expression and other clinicopathological parameters. ICOSL was selected as a dependent variable for multivariate analysis. Results Positive ICOSL expression was identified on the plasma membrane in both cytoplasm and the nucleus of breast cancer cells. Membrane-expressed ICOSL is determined as an independent prognostic factor for OS in breast cancer but without significantly correlating with other clinicopathologic parameters such as age, menopausal status, depth of invasion, lymph node metastasis status, histologic classification, etc. Conclusion Our study suggests that the up-regulated expression of ICOSL protein in breast tumor cells can be associated with poor prognoses in invasive breast carcinomas

A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections, making them ineffectively processed by current Graph Neural Networks (GNNs). To tackle this issue, researchers proposed a variety of Geometric Graph Neural Networks equipped with invariant/equivariant properties to better characterize the geometry and topology of geometric graphs. Given the current progress in this field, it is imperative to conduct a comprehensive survey of data structures, models, and applications related to geometric GNNs. In this paper, based on the necessary but concise mathematical preliminaries, we provide a unified view of existing models from the geometric message passing perspective. Additionally, we summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation. We also discuss the challenges and future potential directions of Geometric GNNs at the end of this survey