Modules identification in gene positive networks of hepatocellular carcinoma using pearson agglomerative method and Pearson cohesion coupling modularity

In this study, a gene positive network is proposed based on a weighted undirected graph, where the weight represents the positive correlation of the genes. A Pearson agglomerative clustering algorithm is employed to build a clustering tree, where dotted lines cut the tree from bottom to top leading to a number of subsets of the modules. In order to achieve better module partitions, the Pearson correlation coefficient modularity is addressed to seek optimal module decomposition by selecting an optimal threshold value. For the liver cancer gene network under study, we obtain a strong threshold value at 0.67302, and a very strong correlation threshold at 0.80086. On the basis of these threshold values, fourteen strong modules and thirteen very strong modules are obtained respectively. A certain degree of correspondence between the two types of modules is addressed as well. Finally, the biological significance of the two types of modules is analyzed and explained, which shows that these modules are closely related to the proliferation and metastasis of liver cancer. This discovery of the new modules may provide new clues and ideas for liver cancer treatment

Multiangle social network recommendation algorithms and similarity network evaluation

Multiangle social network recommendation algorithms (MSN) and a new assessmentmethod, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithmfromresource point (UBR), user-based algorithmfromtag point (UBT), resource-based algorithm fromtag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels

Graduate Recital: Zhiyuan Gao, Horn; Lu Witzig, Piano; Joohee Jeong, Piano; Jinyu Zhang, Piano; April 7, 2024

Kemp Recital HallApril 7, 2024Sunday Evening6:30 p.m

Preparation and Synergy of Supported Ru0 and Pd0 for Rapid Chlorate Reduction at pH 7

Chlorate (ClO3–) is a common water pollutant due to its gigantic scale of production, wide applications in agriculture and industry, and formation as a toxic byproduct in various water treatment processes. This work reports on the facile preparation, mechanistic elucidation, and kinetic evaluation of a bimetallic catalyst for highly active ClO3– reduction into Cl–. Under 1 atm H2 and 20 °C, PdII and RuIII were sequentially adsorbed and reduced on a powdered activated carbon support, affording Ru0–Pd0/C from scratch within only 20 min. The Pd0 particles significantly accelerated the reductive immobilization of RuIII as \u3e55% dispersed Ru0 outside Pd0. At pH 7, Ru–Pd/C shows a substantially higher activity of ClO3– reduction (initial turnover frequency \u3e13.9 min–1 on Ru0; rate constant at 4050 L h–1 gmetal–1) than reported catalysts (e.g., Rh/C, Ir/C, Mo–Pd/C) and the monometallic Ru/C. In particular, Ru–Pd/C accomplished the reduction of concentrated 100 mM ClO3– (turnover number \u3e 11,970), whereas Ru/C was quickly deactivated. In the bimetallic synergy, Ru0 rapidly reduces ClO3– while Pd0 scavenges the Ru-passivating ClO2– and restores Ru0. This work demonstrates a simple and effective design for heterogeneous catalysts tailored for emerging water treatment needs

Track Anything: Segment Anything Meets Videos

Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos. To be detailed, given a video sequence, only with very little human participation, i.e., several clicks, people can track anything they are interested in, and get satisfactory results in one-pass inference. Without additional training, such an interactive design performs impressively on video object tracking and segmentation. All resources are available on {https://github.com/gaomingqi/Track-Anything}. We hope this work can facilitate related research.Comment: Tech-repor

Target-Driven Structured Transformer Planner for Vision-Language Navigation

Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Driven Structured Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments). In addition, we design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning. Experimental results demonstrate that our TD-STP substantially improves previous best methods' success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks, respectively. Our code is available at https://github.com/YushengZhao/TD-STP