Biology subjects

Li, J. K.

Publications and source records attributed to Li, J. K..

6 recordsLinked to original sources

bioRxiv2026

DICAROS: Diffeomorphic Ancestral Shape Reconstruction on Phylogenies

Reconstructing ancestral morphologies on a phylogenetic tree is a central task in evolutionary morphometrics. Established reconstruction methods, including multivariate Brownian-motion approaches, rely on linear assumptions and do not directly model the correlations between landmarks within a shape, which can oversimplify the reconstructed morphology. The DICAROS method (Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes; Severinsen et al., 2026) instead fuses sibling shapes along branches with large-deformation diffeomorphic (LDDMM) landmark dynamics that model these correlations, so that ancestors remain on the shape manifold. DICAROS was shown to outperform ordinary least-squares, Brownian-motion, and penalized-likelihood reconstruction, particularly on non-symmetric trees. The dicaros package repackages that pipeline as a documented, pip-installable tool that runs on arbitrary landmark datasets from a single command. It handles 2D and 3D landmarks, Newick and NEXUS trees, a choice of Euclidean or Frechet species means, optional anchor-based alignment, and tips backed by a single specimen, and it returns the reconstructed shapes for all nodes together with the tree relabelled at its internal nodes. We demonstrate dicaros on two new datasets: a 2D leaf dataset (217 species) and a 3D guenon skull dataset (22 species).

evolutionary biology↗

bioRxiv2026

A hierarchical Bayesian framework accommodates intraspecific and interspecific variation in multivariate traits

Phylogenetic comparative methods are a critical tool in biology, providing the framework to test evolutionary hypotheses of phenotypic diversification. Accommodating intraspecific variation in multivariate analyses is critical for accurate evolutionary inference, but current methods that incorporate intraspecific variation either 1) assume that traits evolve independently or 2) that all taxa share the same intraspecific covariance structure. Violations of these assumptions can produce biased estimates of evolutionary parameters. Here, we introduce a hierarchical Bayesian framework for multivariate traits that jointly estimates taxon-specific intraspecific covariance structures alongside the underlying evolutionary process. This framework propagates uncertainty from sample size discrepancies and missing data, enabling the incorporation of highly variable morphological traits into phylogenetic analyses. Analysis of simulated data confirms that the model and implementation are well calibrated under the assumed generative model, including challenging datasets with more traits than individuals and substantial missing observations. Applied to perikymata spacing across the great ape clade, including modern humans and Neandertals, the framework recovers intraspecific covariance structures that differ among taxa and yields evolutionary rate estimates markedly more uniform across the tooth crown than those obtained when taxon means are fixed. Our method, which is applicable to other multivariate traits, provide a flexible, tractable approach to joint estimation of intraspecific variation and evolutionary process in multivariate traits.

evolutionary biology↗

bioRxiv2026

STEM-LM: Spatio-Temporal Ecological Modeling via Masked Language Model for Joint Species Distribution

Joint species distribution models (JSDMs) are central to biodiversity forecasting and conservation decision-making. As ecological datasets grow in size, dimensionality, and spatio-temporal resolution, there is a need for flexible yet scalable JSDMs tailored to large-scale species observation data. Recent advances in masked language modeling for text and genomics suggest a natural alternative: by treating each species presence or absence as a token, and a sites species assemblage together with its spatio-temporal and ecological covariates as a sentence, we can learn joint co-occurrence structure by reconstructing masked species from their neighboring sites. We propose STEM-LM1, a Transformer-based JSDM that frames joint species distribution modeling as masked language modeling. By varying the masking rate during training, a single trained model supports both purely spatiotemporal/ecological prediction and conditioning on arbitrary subsets of observed species for joint co-occurrence inference at a given site. On a North American butterfly and a global plant distribution dataset, STEM-LM performs better or on par with other statistical and deep-learning based methods in terms of discriminative ranking, while producing substantially better rank-calibrated occurrence probabilities. Utilizing partial species observations at the same site greatly enhances prediction performance.

ecology↗

bioRxiv2025

Principal Components Analysis fails to recover phylogenetic structure in hominins

ObjectivesPaleoanthropologists often utilize geometric morphometrics and principal components analysis (PCA) to interpret shape variation within the hominin fossil record. It is common practice to interpret proximity in principal components (PC) space among taxa as indicative of not just morphological, but also phylogenetic affinity. This interpretation, however, has not been directly evaluated for hominins. Materials and MethodsFirst, we inferred the posterior distribution of hominin phylogenetic trees and subsampled trees from this distribution. On these phylogenies, we simulated 2D and 3D geometric morphometric datasets and traditional morphological datasets, containing traits analogous to measurements of size or length, with varying numbers of landmarks or traits and evolutionary rates. On each dataset, we conducted a PCA and used neighbor-joining to infer evolutionary relationships from the PC scores of each taxon. We measure the difference between the PCA tree and sampled tree with subtree pruning and regrafting distance and Robinson-Foulds distance. ResultsPCA trees inferred from traditional morphometric data were identical to the sampled tree in 0.11% of datasets when we only considered PC axes 1 and 2, and in 2.9% of datasets when we considered all axes. No PCA tree inferred from any of the 2,400,000 shape datasets was identical to the sampled tree, regardless of the number of axes. DiscussionPhylogenetic interpretations of the hominin fossil record based on proximity in PC space are inherently flawed and likely to be erroneous. Arguments in the hominin systematics literature based on PCA should therefore be reevaluated using phylogenetically-informed alternatives.

evolutionary biology↗

bioRxiv2023

Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation

We describe the following shared data from N=103 healthy adults who completed a broad set cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90- minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. Subjects also completed resting state scans. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard.

neuroscience↗

bioRxiv2022

A modular dCas9-based recruitment platform for combinatorial epigenome editing

CRISPR-dCas9 based targeted epigenome editing tools allow precise manipulation and functional investigation of various genome modifications. However, these tools often display substantial context dependency, with highly variable efficacy between target genes and cell types, potentially due to underlying variation in the chromatin modifications present. While simultaneous recruitment of multiple distinct effector chromatin regulators has improved efficacy, these systems typically lack control over which effectors bind and their spatial organisation. To overcome this we have created a new modular combinatorial epigenome editing platform, called SSSavi. This system acts as an interchangeable and reconfigurable docking platform fused to dCas9 to enable simultaneous recruitment of up to four different effectors, allowing precise control and reconfiguration of the effector composition and spatial ordering of their binding. We demonstrate the activity and specificity of the SSSavi system and compare it to existing multi-effector targeting systems, establishing its efficacy. Furthermore, by altering the spatial ordering of effector recruitment, across multiple target genes and cell lines, we demonstrate the importance of effector recruitment order for effective transcriptional regulation. Together, this system offers the capacity to explore effector co-recruitment to specific loci to potentially enhance the manipulation of chromatin contexts previously resistant to targeted epigenomic editing.

synthetic biology↗