Porous structures are essential in a variety of fields such as materials science and chemistry. To retrieve porous materials efficiently, novel descriptors are required to quantify the geometric and topological features. In this paper, we present a novel framework to create a descriptor that incorporates both topological and geometric information of a porous structure. To capture geometric information, we keep track of the birthtime and deathtime of the persistentfeatures of a real-valued function on the surface that evolves with a parameter. Then, we generate the corresponding persistentfeaturediagram (DgmPF) and convert it into a vector called persistencefeaturedescriptor (PFD). To extract topological information, we sample points from the pore surface and compute the corresponding persistence diagram, which is then transformed into the Persistence B-Spline Grids (PBSG). Our proposed descriptor, namely persistentgeometry−topologydescriptor (PGTD), is obtained by concatenating PFD with PBSG. In our experiments, we use the heat kernel signature (HKS) as the real-valued function to compute the descriptor. We test the method on a synthetic porous dataset and a zeolite dataset and find that it is competitive compared to other descriptors based on HKS and advanced topological descriptors.

MOTIVATION: Gene alternative splicing plays an important role in development, tissue specialization and disease and differences in splicing patterns can reveal important factors for phenotypic differentiation. While multiple computational methods exist to determine splicing differences, there is a need for user-friendly visualizations that present an intuitive view of the data and work across methods. RESULTS: We developed a toolkit, Jutils, for visualizing differential splicing events at the intron (splice junction) level. Jutils is method-agnostic, converting individual tools' output into a unified representation and using it to create visualizations. Jutils creates three types of visualizations, namely heatmaps of absolute and Z-score normalized splice ratios, sashimi plots and Venn diagrams of results from multiple comparisons. Jutils is lightweight, relying solely on the unified data file for visualizations. AVAILABILITY AND IMPLEMENTATION: Jutils is implemented in Python and is available from https://github.com/Splicebox/Jutils.