Composition and Function of Extracellular Matrix in Development of Skeletal Muscle

Skeletal muscle extracellular matrix (ECM), surrender of muscle fibers, the amount of which is just <5%, appeals less attention in the field of skeletal muscle physiology. Thus, at one time, the function of skeletal muscle ECM was arbitrarily considered as general structural support that is typical in other tissues. However, an increasing number of recent evidences have indicated that the ECM plays a critical role in muscle fiber force transmission, proliferation, differentiation, migration, and polarization of cells. Alterations of molecules within the ECM are involved in fibrosis, muscle aging, regeneration, and myopathies. In this chapter, we review the composition and functions of ECM in skeletal muscle development

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

Two-dimensional nanomaterials, such as graphene, have been extensively studied because of their outstanding physical properties. Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination. However, the optimization process often involves very large number of experiments or simulations which are expensive and time-consuming. In this work, we propose a graphene nanopore optimization framework via the combination of deep reinforcement learning (DRL) and convolutional neural network (CNN) for efficient water desalination. The DRL agent controls the growth of nanopore by determining the atom to be removed at each timestep, while the CNN predicts the performance of nanoporus graphene for water desalination: the water flux and ion rejection at a certain external pressure. With the synchronous feedback from CNN-accelerated desalination performance prediction, our DRL agent can optimize the nanoporous graphene efficiently in an online manner. Molecular dynamics (MD) simulations on promising DRL-designed graphene nanopores show that they have higher water flux while maintaining rival ion rejection rate compared to the normal circular nanopores. Semi-oval shape with rough edges geometry of DRL-designed pores is found to be the key factor for their high water desalination performance. Ultimately, this study shows that DRL can be a powerful tool for material design.Comment: Yuyang Wang and Zhonglin Cao contributed equally to this wor

Fingerprinting Sources of the Sediments Deposited in the Riparian Zone of the Ruxi Tributary Channel of the Three Gorges Reservoir (China)

The riparian zone of the Three Gorges Reservoir serves as a critical transitional zone located between the aquatic and surrounding terrestrial environments. The periodic anti-seasonal alternation of wet and dry periods results in an intensive exchange of substance within the riparian zone. The discrimination of the sources of the sediments deposited within the riparian zone is of fundamental importance for the evaluation of the soil pollution and associated environmental impacts and for the protection of the water quality in the reservoir. In this study, a composite fingerprinting technique has been applied to apportion the sediment sources for the riparian zone with different elevations, ranging between 145—155, 155–165, and 165–175 m in a typical tributary channel. From a sediment perspective, the sediments suspended from the Yangtze mainstream represent the primary sources of the riparian deposits. From a contamination perspective, the sediment input from the Ruxi tributary channel represents an important source of pollution for the riparian environment. More effective sediment and sediment-associated contaminant control plans are needed to reduce the potential environmental problems of the riparian zone

The arabidopsis RCC1 family protein TCF1 regulates freezing tolerance and cold acclimation through modulating lignin biosynthesis

Cell water permeability and cell wall properties are critical to survival of plant cells during freezing, however the underlying molecular mechanisms remain elusive. Here, we report that a specifically cold-induced nuclear protein, Tolerant to Chilling and Freezing 1 (TCF1), interacts with histones H3 and H4 and associates with chromatin containing a target gene, BLUE-COPPER-BINDING PROTEIN (BCB), encoding a glycosylphosphatidylinositol-anchored protein that regulates lignin biosynthesis. Loss of TCF1 function leads to reduced BCB transcription through affecting H3K4me2 and H3K27me3 levels within the BCB gene, resulting in reduced lignin content and enhanced freezing tolerance. Furthermore, plants with knocked-down BCB expression (amiRNA-BCB) under cold acclimation had reduced lignin accumulation and increased freezing tolerance. The pal1pal2 double mutant (lignin content reduced by 30% compared with WT) also showed the freezing tolerant phenotype, and TCF1 and BCB act upstream of PALs to regulate lignin content. In addition, TCF1 acts independently of the CBF (C-repeat binding factor) pathway. Our findings delineate a novel molecular pathway linking the TCF1-mediated cold-specific transcriptional program to lignin biosynthesis, thus achieving cell wall remodeling with increased freezing tolerance

Research on the Approach and Strategy of Traditional Logistics Enterprise Transformation Under the Context of the Internet

In order to study the approach and strategy of traditional logistics enterprises to transform to green logistics enterprises under the background of the Internet. In Sichuan province, 1,203 samples were taken and analyzed by SPSS data. Finally, the influence factors of consumers’ usage intentions are obtained. Based on the influence factors, the packaging and lines are designed to ensure the recycle. At the same time, the damage detection function of relevant magnetic stripe is used as auxiliary function, collecting the data information of consumers

Neural Network Predicts Ion Concentration Profiles under Nanoconfinement

Modeling the ion concentration profile in nanochannel plays an important role in understanding the electrical double layer and electroosmotic flow. Due to the non-negligible surface interaction and the effect of discrete solvent molecules, molecular dynamics (MD) simulation is often used as an essential tool to study the behavior of ions under nanoconfinement. Despite the accuracy of MD simulation in modeling nanoconfinement systems, it is computationally expensive. In this work, we propose neural network to predict ion concentration profiles in nanochannels with different configurations, including channel widths, ion molarity, and ion types. By modeling the ion concentration profile as a probability distribution, our neural network can serve as a much faster surrogate model for MD simulation with high accuracy. We further demonstrate the superior prediction accuracy of neural network over XGBoost. Lastly, we demonstrated that neural network is flexible in predicting ion concentration profiles with different bin sizes. Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement

Momentum Gradient-based Untargeted Attack on Hypergraph Neural Networks

Hypergraph Neural Networks (HGNNs) have been successfully applied in various hypergraph-related tasks due to their excellent higher-order representation capabilities. Recent works have shown that deep learning models are vulnerable to adversarial attacks. Most studies on graph adversarial attacks have focused on Graph Neural Networks (GNNs), and the study of adversarial attacks on HGNNs remains largely unexplored. In this paper, we try to reduce this gap. We design a new HGNNs attack model for the untargeted attack, namely MGHGA, which focuses on modifying node features. We consider the process of HGNNs training and use a surrogate model to implement the attack before hypergraph modeling. Specifically, MGHGA consists of two parts: feature selection and feature modification. We use a momentum gradient mechanism to choose the attack node features in the feature selection module. In the feature modification module, we use two feature generation approaches (direct modification and sign gradient) to enable MGHGA to be employed on discrete and continuous datasets. We conduct extensive experiments on five benchmark datasets to validate the attack performance of MGHGA in the node and the visual object classification tasks. The results show that MGHGA improves performance by an average of 2% compared to the than the baselines