MOTIVATION: Blood-based profiling of tumor DNA ("liquid biopsy") offers great prospects for non-invasive early cancer diagnosis and clinical guidance, but requires further computational advances to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific tasks: (i) applying longitudinal ctDNA data to refine phylogeny models of clonal evolution, and (ii) quantifying changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these through a probabilistic framework for optimally identifying markers and using them to characterize clonal evolution. RESULTS: We first estimate a density over clonal tree models using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these models over successive longitudinal samples. We use the resulting framework for modeling and refining tree densities to pose a set of optimization problems for selecting ctDNA markers to maximize measures of utility for reducing uncertainty in phylogeny models and quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining tree densities and inferring clonal frequencies. Application to real tumor data further demonstrated the methods' effectiveness in refining a lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of somatic evolution and tumor progression. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMUSchwartzLab/Mase-phi.git. (DOI: 10.5281/zenodo.14776163).

In this paper, a novel hybrid positioning system (HPS) is proposed by combining Bluetooth direction finding and GNSS signals for seamless indoor – outdoor positioning and asset tracking applications. We are demonstrating this novel hybrid system using a recent commercially available tracking device being capable of transmitting Bluetooth packets with continuous tone extension (CTE) and simultaneously receiving standard precision GNSS signals. The fusion of the Bluetooth and GNSS position estimates is applied after collecting indoor – outdoor measurement data from three different experimental set-ups, and emulating the seamlessness of such HPS operating in real time scenarios.

Hybridization of Global Navigation Satellite Systems (GNSS) and fifth generation (5G) cellular positioning is foreseen as a key solution to fulfill high-accuracy positioning requirements in future use cases, such as autonomous vehicles. The evaluation of the hybrid positioning capabilities implies the physical-layer simulation of observables from both GNSS and 5G technologies. In order to ease the complexity of the resulting system-level simulations, a physical-layer abstraction of GNSS and 5G ranging observables is here proposed. The abstraction of GNSS ranging observables is based on a Gaussian-distributed model of the errors sources, while the abstraction of 5G ranging observables is based on the interpolation of the cumulative density function (CDF) of the ranging errors for certain propagation conditions and signal-to-noise (SNR) levels. Thanks to the exploitation of the proposed physical-layer abstraction, low-complexity system- level simulations are performed to assess the positioning capabilities of GNSS and 5G downlink time-difference of arrival (DL-TDoA) in urban macro-cell (UMa) environments. The simulation results indicate the need to adopt hybrid solutions based on multiple GNSS constellations and 5G DL-TDoA with 100-MHz bandwidth, in order to ensure a horizontal positioning accuracy below 5 m for 95% of cases in outdoor urban environments.