Coordinate-free formation control of multi-agent systems using rooted graphs

This paper studies how to control large formations of autonomous agents in the plane, assuming that each agent is able to sense relative positions of its neighboring agents with respect to its own local coordinate system. We tackle the problem by adopting two types of controllers. First, we use the classical gradient-based controllers on three leader agents to meet their distance constraints. Second, we develop other type of controllers for follower agents: utilizing the properties of rooted graphs, one is able to design linear controllers incorporating relative positions between the follower agents and their neighbors, to stabilize the overall large formations. The advantages of the proposed method are fourfold: (i) fewer constraints on neighboring relationship graphs; (ii) simplicity of linear controllers for follower agents; (iii) global convergence of the overall formations; (iv) implementation in local coordinate systems, in no need of a global coordinate system. Numerical simulations show the effectiveness of the proposed method

A Network Approach to Predict Pathogenic Genes for Fusarium graminearum

Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which demonstrate the effectiveness of the proposed method. The results presented in this paper not only can provide guidelines for future experimental verification, but also shed light on the pathogenesis of the destructive fungus F. graminearum

Scaling of global input–output networks

Examining scaling patterns of networks can help understand how structural features relate to the behavior of the networks. Input–output networks consist of industries as nodes and inter-industrial exchanges of products as links. Previous studies consider limited measures for node strengths and link weights, and also ignore the impact of dataset choice. We consider a comprehensive set of indicators in this study that are important in economic analysis, and also examine the impact of dataset choice, by studying input–output networks in individual countries and the entire world. Results show that Burr, Log-Logistic, Log-normal, and Weibull distributions can better describe scaling patterns of global input–output networks. We also find that dataset choice has limited impacts on the observed scaling patterns. Our findings can help examine the quality of economic statistics, estimate missing data in economic statistics, and identify key nodes and links in input–output networks to support economic policymaking

Radiation produces differential changes in cytokine profiles in radiation lung fibrosis sensitive and resistant mice

<p>Abstract</p> <p>Background</p> <p>Recent research has supported that a variety of cytokines play important roles during radiation-induced lung toxicity. The present study is designed to investigate the differences in early cytokine induction after radiation in sensitive (C57BL/6) and resistant mice (C3H).</p> <p>Results</p> <p>Twenty-two cytokines in the lung tissue homogenates, bronchial lavage (BAL) fluids, and serum from 3, 6, 12, 24 hrs to 1 week after 12 Gy whole lung irradiation were profiled using a microsphere-based multiplexed cytokine assay. The majority of cytokines had similar baseline levels in C57BL/6 and C3H mice, but differed significantly after radiation. Many, including granulocyte colony-stimulating factor (G-CSF), interleukin-6 (IL-6), and keratinocyte-derived chemokine (KC) were elevated significantly in specimens from both strains. They usually peaked at about 3–6 hrs in C57BL/6 and 6–12 hrs in C3H. At 6 hrs in lung tissue, G-CSF, IL-6, and KC increased 6, 8, and 11 fold in C57BL/6 mice, 4, 3, and 3 fold in the C3H mice, respectively. IL-6 was 10-fold higher at 6 hrs in the C57BL/6 BAL fluid than the C3H BAL fluid. MCP-1, IP-10, and IL-1α also showed some differences between strains in the lung tissue and/or serum. For the same cytokine and within the same strain of mice, there were significant linear correlations between lung tissue and BAL fluid levels (R²ranged 0.46–0.99) and between serum and tissue (R²ranged 0.56–0.98).</p> <p>Conclusion</p> <p>Radiation induced earlier and greater temporal changes in multiple cytokines in the pulmonary fibrosis sensitive mice. Positive correlation between serum and tissue levels suggests that blood may be used as a surrogate marker for tissue.</p

IP102: A large-scale benchmark dataset for insect pest recognition

Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upper level category. Furthermore, we perform several baseline experiments on the IP102 dataset, including handcrafted and deep feature based classification methods. Experimental results show that this dataset has the challenges of interand intra- class variance and data imbalance. We believe our IP102 will facilitate future research on practical insect pest control, fine-grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/ xpwu95/IP10