A closer look at interacting dark energy with statefinder hierarchy and growth rate of structure

We investigate the interacting dark energy models by using the diagnostics of statefinder hierarchy and growth rate of structure. We wish to explore the deviations from $\Lambda$CDM and to differentiate possible degeneracies in the interacting dark energy models with the geometrical and structure growth diagnostics. We consider two interacting forms for the models, i.e., $Q_1=\beta H\rho_c$ and $Q_2=\beta H\rho_{de}$, with $\beta$ being the dimensionless coupling parameter. Our focus is the I$\Lambda$CDM model that is a one-parameter extension to $\Lambda$CDM by considering a direct coupling between the vacuum energy ($\Lambda$) and cold dark matter (CDM), with the only additional parameter $\beta$. But we begin with a more general case by considering the I$w$CDM model in which dark energy has a constant $w$ (equation-of-state parameter). For calculating the growth rate of structure, we employ the "parametrized post-Friedmann" theoretical framework for interacting dark energy to numerically obtain the $\epsilon(z)$ values for the models. We show that in both geometrical and structural diagnostics the impact of $w$ is much stronger than that of $\beta$ in the I$w$CDM model. We thus wish to have a closer look at the I$\Lambda$CDM model by combining the geometrical and structural diagnostics. We find that the evolutionary trajectories in the $S^{(1)}_3$--$\epsilon$ plane exhibit distinctive features and the departures from $\Lambda$CDM could be well evaluated, theoretically, indicating that the composite null diagnostic $\{S^{(1)}_3, \epsilon\}$ is a promising tool for investigating the interacting dark energy models.Comment: 17 pages, 4 figures; accepted for publication in JCA

Screening of Potential Hub Genes in Glioma Progression Based on Bioinformatics Analysis

Objectives: Glioma is the most common primary tumor of the central nervous system, and its therapeutic effect is not optimistic. In recent years, related therapeutic technologies have developed rapidly, but unfortunately, the improvement of clinical therapeutic effect is not satisfactory. In addition to conventional therapies, there are some attractive therapies, such as biological therapy (immunotherapy), gene therapy, etc[1]. Therefore, searching for potential target genes of glioma is of great significance for developing new therapeutic directions and designing new biomarkers[2]. Methods: Download gene expression data set, GSE137902 gelatin and GSE13790 matrix through NCBI-G to screen overlapping differential expression genes (DEGs). In order to identify central genes from these genes, we conducted protein protein interaction (PPI) network. To further explore the potential mechanism of central genes in glioma, we performed gene ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analysis. Then get the intersection of key genes according to five algorithms of Closeness Degree EPC MCC Stress. The intersection is obtained through GSE117423, GSE188256 and GSE90598 in geo database, and finally verified through Receiver Operating Characteristic (ROC) curve. Results: A total of 1274 differentially expressed genes are identified, and then 309 genes are obtained by intersection of the two. 16 Hub genes were obtained, and then the intersection of the two genes with GSE117423, GES188256 and GSE90598 genes was verified to obtain the key gene TIMP1 of glioma. Made the ROC curve of key gene.The intersection with hub gene was determined to identify TIMP1 as the key gene. Conclusion: The DEGs and Hub genes and signal pathways found in this study can confirm that the key gene TIMP1 is closely related to the occurrence and evolution of glioma, and provide candidate targets for the diagnosis and treatment of glioma

Dimethano­lbis[N′-(3-pyridylmethyl­ene)benzohydrazide]sodium(I) iodide

The molecule of the title compound, [Na(C13H11N3O)2(CH3OH)2]I, is non-planar, with the Na atom chelated by the O atoms and the N atoms of two N′-(3-pyridylmethyl­ene)benzohydrazide ligands and both O atoms of two methanol ligands. The asymmetric unit consists of one half-mol­ecule. The Na atom is located on a crystallographic centre of inversion. The six-coordinate Na atom adopts a distorted octa­hedral coordination. In the crystal structure, inter­molecular N—H⋯I and O—H⋯N hydrogen bonds link the mol­ecules into a two-dimensional network

Highly sensitive and selective CO sensor using a 2.33 μm diode laser and wavelength modulation spectroscopy

A ppm-level CO sensor based on a 2f wavelength modulation spectroscopy (2f-WMS) technique was developed for the application of SF6 decomposition analysis in an electric power system. A detailed investigation of the optimum target line selection was carried out to avoid spectral interference from high purity SF6 in a wide wavelength range. A diode laser emitting at 2.33 μm and a 14.5-m multipass gas cell (MGC) was employed to target the R(6) line of the CO first overtone band and increase the optical path, respectively, thus resulting in a minimum detection sensitivity of 1 ppm. A Levenberg-Marquardt nonlinear least-squares fit algorithm makes full use of the information from all data points of the 2f spectrum and as a result, a measurement precision of ~40 ppb was achieved with a data update rate of 0.6 s. The sensor performance was also evaluated in terms of the gas flow rate, stability, and linearity. The results showed that the best operating condition with a precision of 6 ppb can be achieved by increasing the gas flow rate to the value that matches the optimum averaging time of 48 s

Distributed Graph Neural Network Training: A Survey

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review on the optimization techniques for the distributed execution of GNN training. In this survey, we analyze three major challenges in distributed GNN training that are massive feature communication, the loss of model accuracy and workload imbalance. Then we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories that are GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol. We carefully discuss the techniques in each category. In the end, we summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and CPU-clusters, respectively, and give a discussion about the future direction on distributed GNN training

Quality-of-life outcomes and unmet needs between ileal conduit and orthotopic ileal neobladder after radical cystectomy in a Chinese population: a 2-to-1 matched-pair analysis

Peri-operative outcomes. (DOCX 15 kb