Biology subjects

Guang, Z.

Publications and source records attributed to Guang, Z..

3 recordsLinked to original sources

bioRxiv2026

Reticulation-Aware Dissimilarities for Phylogenetic Networks

Dissimilarity measures play a critical role in phylogenetics. They help quantify the accuracy of inferences when the ground truth is known, allow comparisons of inferences obtained using different methods, and provide a tool for summarizing sets of phylogenies. A wide array of dissimilarity measures has been introduced for phylogenetic trees over the past half-century, and they vary in their mathematical properties, empirical behavior, and computational complexity. When the evolutionary history involves reticulate processes, such as hybridization and horizontal gene transfer, it is represented by phylogenetic networks. Although the study of phylogenetic network dissimilarity is less established than its tree counterpart, the past two decades have seen an increasing number of measures developed for phylogenetic networks. However, while some have established mathematical properties---for example, metricity on certain classes of phylogenetic networks---none properly separates reticulation disagreement from tree-like disagreement. This is the rationale behind the introduction of the hybrid ancestry error (HAE) measure by Dai and Molloy. The HAE measure, however, is too simplistic to distinguish different reticulation scenarios. In this work, we propose a novel family of network dissimilarity measures that explicitly account for reticulation differences while being more informative than HAE. We prove that our measures are pseudometrics on rooted binary phylogenetic networks on a fixed taxon set, with two instantiations yielding metrics on binary level-1 networks. Empirically, the measures remain sensitive to reticulation errors as network size grows and respond to changes in hybrid identity and parental placement. Calibration experiments further characterize the measures' behavior under network edits of various magnitudes. Data Availability: The dissimilarities are implemented in the PhyNetPy library, available at https://github.com/NakhlehLab/PhyNetPy. All the data simulated for the study is available at https://doi.org/10.5281/zenodo.23933421. Funding: This work was supported in part by the NSF grants DMS/NIGMS-2153704 and DBI-2420499. Declaration of AI-assisted technologies: During the preparation of this work, the authors used OpenAI's Codex (GPT-6 Astra) and Anthropic's Claude (Fable 5) to assist with editing manuscript text, developing proofs and analysis code, and preparing figures. The authors reviewed and edited the resulting material, verified the analyses, and take full responsibility for the content of this article.

bioinformatics↗

bioRxiv2026

DIMPLE: A Tree-Guided Divide-and-Conquer Framework for ILS-Aware Phylogenetic Network Inference on Large-Scale Data

Inferring evolutionary histories of species provides a foundation for comparative genomics and evolutionary analyses. Extensive evidence of reticulate evolution motivates the inference of phylogenetic networks, which represent relationships that species trees cannot capture. However, accurate reconstruction of large phylogenetic networks remains a major challenge because of the vast space of possible network topologies. Here, we propose DIMPLE, a divide-and-conquer framework for phylogenetic network inference without an explicit restriction on network level. DIMPLE uses a tree-of-blobs (TOB), which represents the tree-like relationships between regions of potential reticulation while leaving relationships within those regions unresolved. Within each unresolved region, DIMPLE partitions the set of taxa into pairwise-disjoint subsets, each containing at most a user-specified number of taxa. Multiple division runs are allowed, and subsets from different runs may overlap. DIMPLE infers a subnetwork on each subset, merges the subnetworks within each region, and uses the TOB to assemble the resulting networks into a network on the full taxon set. Experiments with true inputs demonstrate accurate merging, with improved reticulation recovery over existing methods on higher-level networks. With estimated inputs, DIMPLE achieves competitive topological accuracy and more consistently recovers hybrid ancestry than competing methods across varying levels of network complexity. Its computational requirements support analyses of hundreds of taxa. Applications to fern and gymnosperm datasets recover documented hybridization and previously reported phylogenetic conflict.

bioinformatics↗

bioRxiv2025

Label-Free, Real-Time, In Vivo Optical Biopsy with a Handheld Quantitative Phase Microscope

In this work we develop and demonstrate the utility of a compact, handheld quantitative phase imaging microscope that enables label-free, in vivo optical imaging of bulk tissues with clear cellular and subcellular histological detail in real-time. The proposed device overcomes significant challenges in optical imaging for in vivo applications, particularly for clinical human use. The approach uses quantitative oblique back illumination microscopy (qOBM) to obtain quantitative phase information of opaque samples using epi-illumination. The compact handheld probe achieves 0.8 {micro}m lateral resolution, 5 {micro}m axial resolution, 300 {micro}m X 300 {micro}m field of view, and operates at 25Hz in a wide-field (non-scanning) configuration, enabling real-time imaging. The probe is also inexpensive and has no moving components, making it robust. The utility of the probe is demonstrated in (1) human skin in vivo, (2) brain tumor tissue ex vivo from a murine tumor model and from discarded human tissue from neurosurgery, and (3) in vivo using healthy brain tissue from a large animal model (swine), simulating neurosurgical conditions. Given the clear cellular and subcellular histological detail (i.e., "optical biopsy") obtained in real-time, combined with the ease-of-use and low-cost of the system, the proposed device has significant implications for a broad range of clinical applications.

bioengineering↗