Grid-Based Whole Trajectory Clustering in Road Networks Environment

In the data mining of road networks, trajectory clustering of moving objects plays an important role in many applications. Most existing algorithms for this problem are based on every position point in a trajectory and face a significant challenge in dealing with complex and length-varying trajectories. This paper proposes a grid-based whole trajectory clustering model (GBWTC) in road networks, which regards the trajectory as a whole. In this model, we first propose a trajectory mapping algorithm based on grid estimation, which transforms the trajectories in road network space into grid sequences in grid space and forms grid trajectories by recognizing and eliminating redundant, abnormal, and stranded information of grid sequences. We then design an algorithm to extract initial clustering centers based on density weight and improve a shape similarity measuring algorithm to measure the distance between two grid trajectories. Finally, we dynamically allocate every grid trajectory to the best clusters by the nearest neighbor principle and an outlier function. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Silhouette Coefficient to maximize intercluster separation and intracluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated on a real-world dataset in comparison with existing algorithms.

OPEX: Development of a novel overall patient experience measure to facilitate interpretation of comparison effectiveness studies

Objectives A measure that encompasses both benefits and harms at the individual patient level may facilitate comparisons between treatment options and improve shared decision-making. The objective of this study was to develop a patient reported measure to capture overall experience (including both benefits and harms) of treatment using rheumatoid arthritis (RA) as a case example. Methods Hierarchies for treatment benefits are known. Therefore, we developed a hierarchy of adverse events (AEs) using a series of trajectory mapping and paired comparison surveys. We subsequently used these data to construct a paired comparison survey, asking patients to compare options including both a specified level of benefit and an AE. These data were used to generate a hierarchy of overall experience on treatment. Results 782 participants completed a series of three surveys. The trajectory mapping procedure and a paired comparison survey led to the generation of a hierarchy of AEs with nine levels ranging from No AEs to irreversible serious complications. In a third survey, in which AEs were paired with benefits, participants’ ratings generated a 6-level hierarchy of overall experiences ranging from Major improvement + No, mild or manageable AEs (Level 1) to No improvement + Irreversible AEs (Level 6). Conclusions Using a trajectory mapping approach, we developed a patient reported measure representing the distribution of patients’ overall experiences on treatment. The intent of this measure is to enable patients and their physicians to compare the percentage of patients experiencing each level of outcome, from most to least desirable, across treatments.

TIPS: Trajectory Inference of Pathway Significance through Pseudotime Comparison for Functional Assessment of single-cell RNAseq Data

Recent advances in bioinformatics analyses have led to the development of novel tools enabling the capture and trajectory mapping of single-cell RNA sequencing (scRNAseq) data. However, there is a lack of methods to assess the contributions of biological pathways and transcription factors to an overall developmental trajectory mapped from scRNAseq data. In this manuscript, we present a simplified approach for trajectory inference of pathway significance (TIPS) that leverages existing knowledgebases of functional pathways and transcription factor targets to enable further mechanistic insights into a biological process. TIPS returns both the key pathways whose changes are associated with the process of interest, as well as the individual genes that best reflect these changes. TIPS also provides insight into the relative timing of pathway changes, as well as a suite of visualizations to enable simplified data interpretation of scRNAseq libraries generated using a wide range of techniques. The TIPS package can be run through either a web server, or downloaded as a user-friendly GUI run in R, and may serve as a useful tool to help biologists perform deeper functional analyses and visualization of their single-cell and/or large cohort RNAseq data.

Articulated Trajectory Mapping for Reviewing Walking Tours

This paper addresses how to enrich a map-based representation for reviewing walking tours with the features of trajectory mapping and tracing animation. Generally, a trajectory generated by raw GPS data can often be difficult to browse through on a map. To resolve this issue, we first illustrated tangled trajectory lines, inaccurate indoor positioning, and unstable trajectory lines as problems encountered when mapping raw trajectory data. Then, we proposed a new framework that focuses on GPS horizontal accuracy to locate indoor location points and find stopping points on an accelerometer. We also applied a conventional line simplification algorithm to make the trajectory cleaner and then integrated the extracted points with the clean trajectory line. Furthermore, our experiments with some actual logs of walking tours demonstrated that articulated trajectory mapping, which comprises simplification and characterization methods, sufficiently reliable and effective for better reviewing experiences. The paper contributes to the research on cleaning up map-based displays and tracing animations of raw trajectory GPS data by using not only location data but also sensor data that smartphones can collect.