Yip, G. | bioRxiv Science

bioRxiv2025

Unveiling patterns of roadkill of a migratory amphibian in Hong Kong with implications for mitigation

Road-related mortality, particularly wildlife-vehicle collisions, poses a significant threat to amphibian populations, with billions of individuals falling victim annually. The rapid urban development in Hong Kong, China, including the forthcoming construction of a "Northern Metropolis" area, is expected to bring over 2.5 million new residents, potentially increasing traffic and posing a heightened threat to local amphibians during their migration periods. To help prevent future biodiversity loss, our study focuses on the near-threatened newt species Paramesotriton hongkongensis in Hong Kong, investigating the spatiotemporal distribution of roadkill during migration seasons. Weekly surveys at four hotspots revealed 1,563 animal carcasses, the majority of which were P. hongkongensis. Factors triggering mass mortality events were explored, although no significant correlations were found. Prediction models demonstrated moderate accuracy in detecting mass mortality events, indicating a need for further refinement. Recommendations for site-specific mitigation measures to protect amphibians during their migrations are discussed, with an emphasis on the importance of conducting fine-scale surveys for effective conservation strategies.

ecology↗

bioRxiv2021

VIA: Generalized and scalable trajectory inference in single-cell omics data

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g. cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

bioinformatics↗