RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems.

Storage Method of Multi-Channel Lidar Data Based on Tree Structure

The multi-channel lidar has the characteristics of fast acquisition speed, large data volume, high dimension, and strong real-time storage, which makes it difficult to be met using the traditional lidar data storage methods. This paper presents a novel approach to store and convert the multi-channel lidar data by traversal method of the tree structure and binary code. In the proposed approach, a tree structure is constructed based on the multi-dimensional characteristics of multi-channel lidar data and the hierarchical relationship between them. The adjacency table storage structure data in the memory is used to generate the sub-tree of the multi-channel lidar data. The results show that the proposed tree structure approach can save the storage capacity and improve the retrieval speed, which can meet the needs of efficient storage and retrieval of multi-channel lidar data.

Coalescent angiogenesis—evidence for a novel concept of vascular network maturation

Angiogenesis describes the formation of new blood vessels from pre-existing vascular structures. While the most studied mode of angiogenesis is vascular sprouting, specific conditions or organs favor intussusception, i.e., the division or splitting of an existing vessel, as preferential mode of new vessel formation. In the present study, sustained (33-h) intravital microscopy of the vasculature in the chick chorioallantoic membrane (CAM) led to the hypothesis of a novel non-sprouting mode for vessel generation, which we termed “coalescent angiogenesis.” In this process, preferential flow pathways evolve from isotropic capillary meshes enclosing tissue islands. These preferential flow pathways progressively enlarge by coalescence of capillaries and elimination of internal tissue pillars, in a process that is the reverse of intussusception. Concomitantly, less perfused segments regress. In this way, an initially mesh-like capillary network is remodeled into a tree structure, while conserving vascular wall components and maintaining blood flow. Coalescent angiogenesis, thus, describes the remodeling of an initial, hemodynamically inefficient mesh structure, into a hierarchical tree structure that provides efficient convective transport, allowing for the rapid expansion of the vasculature with maintained blood supply and function during development.

A Legorization method based on 3D color printing trajectory

In this paper, a Legorization method which can reconstruct LEGO model with complex internal and external structures from 3D color printing trajectory is proposed. Different from voxelization methods, by combining advanced adaptive slicing algorithm with building “high-resolution” regions with thin plates, the reconstruction accuracy of initial LEGO units can be guaranteed. Furthermore, the tree structure is employed for automatically generating support structures which can be converted into LEGO support structures. By adopting split assembly appropriately and implementing combination of these parts accurately, the reducing supporting structures can be further simplified. In order to optimize the Legorization scheme, a machine learning method is used to guarantee the quality and efficiency of the reconstruction work. Complex LEGO models are provided to demonstrate the effectiveness of the proposed method.