Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints

The storage, management, and application of massive spatio-temporal data are widely applied in various practical scenarios, including public safety. However, due to the unique spatio-temporal distribution characteristics of re-al-world data, most existing methods have limitations in terms of the spatio-temporal proximity of data and load balancing in distributed storage. There-fore, this paper proposes an efficient partitioning method of large-scale public safety spatio-temporal data based on information loss constraints (IFL-LSTP). The IFL-LSTP model specifically targets large-scale spatio-temporal point da-ta by combining the spatio-temporal partitioning module (STPM) with the graph partitioning module (GPM). This approach can significantly reduce the scale of data while maintaining the model's accuracy, in order to improve the partitioning efficiency. It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results. This method provides a new solution for distributed storage of mas-sive spatio-temporal data. The experimental results on multiple real-world da-tasets demonstrate the effectiveness and superiority of IFL-LSTP

Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features make the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. Therefore, the correlations between vertex features can be better integrated into the model. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning

Experimental and Analytical Investigation on the Nonlinear Behaviors of Glulam Moment-Resisting Joints Composed of Inclined Self-Tapping Screws with Steel Side Plates

Glulam moment-resisting joint composed of inclined self-tapping-screws (STS) with steel side plates were designed and its nonlinear moment-rotational skeleton curve was predicted by taking nonlinear load(P)-deformation(u) relationships of all moment-resisting components into considerations within step-wise linear calculation process. P-u relationships of all moment-resisting components were estimated by the fundamental shear joint tests or appropriate empirical relationships and they were approximated by the tetra polygonal-line curves or bi-linear curves. The extended Normalized Characteristic Loop (NCL) model, which was originally developed for RC construction, was applied to describe the hysteresis loops. For predicting failure load, the design equations for a mechanical joint loaded with inclination to the grain direction were applied. Three replications of T-shaped beam-column joint specimens were fabricated using Canadian spruce glulam beam and column. Connections of steel plates to glulam members were all composed of full-threaded inclined-STS. Static push-pull cyclic loading tests were conducted and observed behaviors were compared with step-wise linear calculation results. Agreements between predicted nonlinear behaviors and observed ones were good on the whole

DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy

Treatment planning is a critical component of the radiotherapy workflow, typically carried out by a medical physicist using a time-consuming trial-and-error manner. Previous studies have proposed knowledge-based or deep learning-based methods for predicting dose distribution maps to assist medical physicists in improving the efficiency of treatment planning. However, these dose prediction methods usuallylack the effective utilization of distance information between surrounding tissues andtargets or organs-at-risk (OARs). Moreover, they are poor in maintaining the distribution characteristics of ray paths in the predicted dose distribution maps, resulting in a loss of valuable information obtained by medical physicists. In this paper, we propose a distance-aware diffusion model (DoseDiff) for precise prediction of dose distribution. We define dose prediction as a sequence of denoising steps, wherein the predicted dose distribution map is generated with the conditions of the CT image and signed distance maps (SDMs). The SDMs are obtained by a distance transformation from the masks of targets or OARs, which provide the distance information from each pixel in the image to the outline of the targets or OARs. Besides, we propose a multiencoder and multi-scale fusion network (MMFNet) that incorporates a multi-scale fusion and a transformer-based fusion module to enhance information fusion between the CT image and SDMs at the feature level. Our model was evaluated on two datasets collected from patients with breast cancer and nasopharyngeal cancer, respectively. The results demonstrate that our DoseDiff outperforms the state-of-the-art dose prediction methods in terms of both quantitative and visual quality

Multi-omics approaches reveal the molecular mechanisms underlying the interaction between Clonorchis sinensis and mouse liver

IntroductionClonorchiasis remains a serious global public health problem, causing various hepatobiliary diseases. However, there is still a lack of overall understanding regarding the molecular events triggered by Clonorchis sinensis (C. sinensis) in the liver.MethodsBALB/c mouse models infected with C. sinensis for 5, 10, 15, and 20 weeks were constructed. Liver pathology staining and observation were conducted to evaluate histopathology. The levels of biochemical enzymes, blood routine indices, and cytokines in the blood were determined. Furthermore, alterations in the transcriptome, proteome, and metabolome of mouse livers infected for 5 weeks were analyzed using multi-omics techniques.ResultsThe results of this study indicated that adult C. sinensis can cause hepatosplenomegaly and liver damage, with the most severe symptoms observed at 5 weeks post-infection. However, as the infection persisted, the Th2 immune response increased and symptoms were relieved. Multi-omics analysis of liver infected for 5 weeks identified 191, 402 and 232 differentially expressed genes (DEGs), proteins (DEPs) and metabolites (DEMs), respectively. Both DEGs and DEPs were significantly enriched in liver fibrosis-related pathways such as ECM-receptor interaction and cell adhesion molecules. Key molecules associated with liver fibrosis and inflammation (Cd34, Epcam, S100a6, Fhl2, Itgax, and Retnlg) were up-regulated at both the gene and protein levels. The top three metabolic pathways, namely purine metabolism, arachidonic acid metabolism, and ABC transporters, were associated with liver cirrhosis, fibrosis, and cholestasis, respectively. Furthermore, metabolites that can promote liver inflammation and fibrosis, such as LysoPC(P-16:0/0:0), 20-COOH-leukotriene E4, and 14,15-DiHETrE, were significantly up-regulated.ConclusionOur study revealed that the most severe symptoms in mice infected with C. sinensis occurred at 5 weeks post-infection. Moreover, multi-omics analysis uncovered predominant molecular events related to fibrosis changes in the liver. This study not only enhances our understanding of clonorchiasis progression but also provides valuable insights into the molecular-level interaction mechanism between C. sinensis and its host liver

Single Image Super Resolution via Neighbor Reconstruction

Super Resolution (SR) is a complex, ill-posed problem where the aim is to construct the mapping between the low and high resolution manifolds of image patches. Anchored neighborhood regression for SR (namely A+  [27]) has shown promising results. In this paper we present a new regression-based SR algorithm that overcomes the limitations of A+ and benefits from an innovative and simple Neighbor Reconstruction Method (NRM). This is achieved by vector operations on an anchored point and its corresponding neighborhood. NRM reconstructs new patches which are closer to the anchor point in the manifold space. Our method is robust to NRM sparsely-sampled points: increasing PSNR by 0.5 dB compared to the next best method. We comprehensively validate our technique on standardised datasets and compare favourably with the state-of-the-art methods: we obtain PSNR improvement of up to 0.21 dB compared to previously-reported work

Mutagenesis breeding research of <i>Lactobacillus brevis</i> of nitrite reduction

The pollution of nitrite in food became one of the focus of food safety issues,the use of biotechnology methods degrading nitrite became hotspot.The primitive strain was Lactobacillus brevis C2,preserved in our laboratory,had the ability to degrade nitrite,through composite mutagenesis of 15 W,254 nm,20 cm ultraviolet mutagenesis (UV) for 120 s and 0.8% diethyl sulfate(DES) in 37℃ mutation for 40 min,after screening,we successfully obtained high efficient strain of nitrite degradation,named UV6-DS2,relative to the starting strain,under the condition of 400 mg/L nitrite,after 12 h degradation,nitrite degradation rate increased from 92.8% to 97.8%,to explore its application in food was able to effectively reduce concentration of nitrite in food

Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning

Network representation learning (NRL) aims to map vertices of a network into a low-dimensional space which preserves the network structure and its inherent properties. Most existing methods for network representation adopt shallow models which have relatively limited capacity to capture highly non-linear network structures, resulting in sub-optimal network representations. Therefore, it is nontrivial to explore how to effectively capture highly non-linear network structure and preserve the global and local structure in NRL. To solve this problem, in this paper we propose a new graph convolutional autoencoder architecture based on a depth-based representation of graph structure, referred to as the depth-based subgraph convolutional autoencoder (DS-CAE), which integrates both the global topological and local connectivity structures within a graph. Our idea is to first decompose a graph into a family of K-layer expansion subgraphs rooted at each vertex aimed at better capturing long-range vertex inter-dependencies. Then a set of convolution filters slide over the entire sets of subgraphs of a vertex to extract the local structural connectivity information. This is analogous to the standard convolution operation on grid data. In contrast to most existing models for unsupervised learning on graph-structured data, our model can capture highly non-linear structure by simultaneously integrating node features and network structure into network representation learning. This significantly improves the predictive performance on a number of benchmark datasets