Joint-tree model and the maximum genus of graphs

The vertex v of a graph G is called a 1-critical-vertex for the maximum genus of the graph, or for simplicity called 1-critical-vertex, if G-v is a connected graph and {\deg}M(G - v) = {\deg}M(G) - 1. In this paper, through the joint-tree model, we obtained some types of 1-critical-vertex, and get the upper embeddability of the Spiral Snm

Application of NIRS in Nutrient Composition Evaluation of \u3cem\u3eLathyrus sativus\u3c/em\u3e

Near Infrared Reflectance Spectroscopy (NIRS) Analysis has applied to measure nutrient composition successfully in many forage species (Jerry et al. 2003). Lathyrus sativus, an annual legume, is the only edible legume among Lathyrus with crude protein up to 30% (Jackson and Yunus 1984). It has high drought tolerance, and can grow well in barren soil. Thus, this species has a great potential as a crude protein source for livestock in arid and semi-arid area. To introduce this species into a grassland system in Northwest of China, developing a rapid method to deter-mine nutrient composition of various accessions is necessary. This study is aimed at calibrating an NIRS in-strument to predict nutritional traits of L. sativus straw and green manure from 50 accessions

Vertex Splitting and Upper Embeddable Graphs

The weak minor G of a graph G is the graph obtained from G by a sequence of edge-contraction operations on G. A weak-minor-closed family of upper embeddable graphs is a set G of upper embeddable graphs that for each graph G in G, every weak minor of G is also in G. Up to now, there are few results providing the necessary and sufficient conditions for characterizing upper embeddability of graphs. In this paper, we studied the relation between the vertex splitting operation and the upper embeddability of graphs; provided not only a necessary and sufficient condition for characterizing upper embeddability of graphs, but also a way to construct weak-minor-closed family of upper embeddable graphs from the bouquet of circles; extended a result in J: Graph Theory obtained by L. Nebesk{\P}y. In addition, the algorithm complex of determining the upper embeddability of a graph can be reduced much by the results obtained in this paper

Disrupted Resting Frontal–Parietal Attention Network Topology Is Associated With a Clinical Measure in Children With Attention-Deficit/Hyperactivity Disorder

Purpose: Although alterations in resting-state functional connectivity between brain regions have been reported in children with attention-deficit/hyperactivity disorder (ADHD), the spatial organization of these changes remains largely unknown. Here, we studied frontal–parietal attention network topology in children with ADHD, and related topology to a clinical measure of disease progression.Methods: Resting-state fMRI scans were obtained from New York University Child Study Center, including 119 children with ADHD (male n = 89; female n = 30) and 69 typically developing controls (male n = 33; female n = 36). We characterized frontal–parietal functional networks using standard graph analysis (clustering coefficient and shortest path length) and the construction of a minimum spanning tree, a novel approach that allows a unique and unbiased characterization of brain networks.Results: Clustering coefficient and path length in the frontal–parietal attention network were similar in children with ADHD and typically developing controls; however, diameter was greater and leaf number, tree hierarchy, and kappa were lower in children with ADHD, and were significantly correlated with ADHD symptom score. There were significant alterations in nodal eccentricity in children with ADHD, involving prefrontal and occipital cortex regions, which are compatible with the results of previous ADHD studies.Conclusions: Our results indicate the tendency to deviate from a more centralized organization (star-like topology) towards a more decentralized organization (line-like topology) in the frontal–parietal attention network of children with ADHD. This represents a more random network that is associated with impaired global efficiency and network decentralization. These changes appear to reflect clinically relevant phenomena and hold promise as markers of disease progression

Quercetin attenuates lipopolysaccharide-induced myocardial cell apoptosis via modulation of cAMP-Epac pathway

Purpose: To investigate the effects and mechanism of action of quercetin (QUE) on sepsis-induced apoptosis of myocardial cells in vitro. Methods: Lipopolysaccharide (LPS) was used to induce apoptosis H9c2 myocardial cells. Apoptosis of H9c2 cells was determined by propidium iodide staining. Knock down of Epac1 was achieved using small interfering RNA (SiEpac1). The levels of associated proteins (Epac1 and Rap1) were evaluated by western blotting. Results: Lipopolysaccharide promoted apoptosis of H9c2 cells and inhibited the activity of cAMP-Epac pathway (p < 0.001 vs. control). Quercetin inhibited caspase 3 activity and apoptosis (p < 0.05 vs. LPS) induced by LPS via activation of cAMP-Epac1 signaling pathway. Moreover, Epac1 knockdown decreased the anti-apoptosis effect of Que, which indicates that Que attenuated apoptosis partly via cAMP-Epac pathway. Conclusion: Que attenuated LPS-induced apoptosis in myocardial cells via activation of cAMP-Epac1 pathway. Therefore, quercetin treatment may serve as a promising strategy in the treatment of sepsisinduced myocardial injury

Dynamic Hand Gesture-Featured Human Motor Adaptation in Tool Delivery using Voice Recognition

Human-robot collaboration has benefited users with higher efficiency towards interactive tasks. Nevertheless, most collaborative schemes rely on complicated human-machine interfaces, which might lack the requisite intuitiveness compared with natural limb control. We also expect to understand human intent with low training data requirements. In response to these challenges, this paper introduces an innovative human-robot collaborative framework that seamlessly integrates hand gesture and dynamic movement recognition, voice recognition, and a switchable control adaptation strategy. These modules provide a user-friendly approach that enables the robot to deliver the tools as per user need, especially when the user is working with both hands. Therefore, users can focus on their task execution without additional training in the use of human-machine interfaces, while the robot interprets their intuitive gestures. The proposed multimodal interaction framework is executed in the UR5e robot platform equipped with a RealSense D435i camera, and the effectiveness is assessed through a soldering circuit board task. The experiment results have demonstrated superior performance in hand gesture recognition, where the static hand gesture recognition module achieves an accuracy of 94.3\%, while the dynamic motion recognition module reaches 97.6\% accuracy. Compared with human solo manipulation, the proposed approach facilitates higher efficiency tool delivery, without significantly distracting from human intents.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Neural Point-based Volumetric Avatar: Surface-guided Neural Points for Efficient and Photorealistic Volumetric Head Avatar

Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications. However, existing methods often struggle to model challenging facial regions (e.g., mouth interior, eyes, hair/beard), resulting in unrealistic and blurry results. In this paper, we propose {\fullname} ({\name}), a method that adopts the neural point representation as well as the neural volume rendering process and discards the predefined connectivity and hard correspondence imposed by mesh-based approaches. Specifically, the neural points are strategically constrained around the surface of the target expression via a high-resolution UV displacement map, achieving increased modeling capacity and more accurate control. We introduce three technical innovations to improve the rendering and training efficiency: a patch-wise depth-guided (shading point) sampling strategy, a lightweight radiance decoding process, and a Grid-Error-Patch (GEP) ray sampling strategy during training. By design, our {\name} is better equipped to handle topologically changing regions and thin structures while also ensuring accurate expression control when animating avatars. Experiments conducted on three subjects from the Multiface dataset demonstrate the effectiveness of our designs, outperforming previous state-of-the-art methods, especially in handling challenging facial regions