Biology subjects

Pierce, C.

Publications and source records attributed to Pierce, C..

7 recordsLinked to original sources

bioRxiv2026

Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials

Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.

genetics↗

bioRxiv2026

Identifying a cooperative catalytic network for efficient esterase catalysis

Active-site redesign frequently yields modest improvements because residues controlling physical steps like substrate binding and product release lie outside the active site. Efficient catalysis requires a cooperative catalytic network of residues that support both the chemical and physical steps of catalysis. Using ancestral hydroxynitrile lyase HNL1, an /{beta}-hydrolase with poor esterase activity, we tested this framework directly. Matching all active-site residues to a proficient esterase improved KM five-fold but left kcat unchanged, confirming that chemical machinery alone is insufficient. Activity-weighted sequence comparison (SigniSite) across ten homologous HNLs and esterases identified 38 positions disfavoring esterase activity. Experimental refinement yielded a minimal set of fifteen substitutions (HNL1-15) with [~]60-fold higher kcat and [~]400-fold higher kcat/KM. Single-substitution reversion analysis confirmed that all fifteen substitutions are essential and provided evidence for strong cooperativity between them. X-ray crystal structures of HNL1 and HNL1-15 reveal three coordinated structural changes: reshaping the substrate-binding pocket to favor productive ester binding, restoring access to the oxyanion hole, and opening an additional tunnel for product egress and water entry. These changes arise through backbone rearrangements and altered flexibility rather than direct active-site contacts, explaining why the responsible positions escape conservation-based detection. Because cooperativity masks individual contributions, engineering such networks may require step-specific assays -- measuring binding, acylation, or product release directly -- rather than screening composite kcat.

biochemistry↗

bioRxiv2026

HYPER-Net: Physics-Conditioned Self-Supervised Reconstruction for Fourier Light-Field Microscopy

The rapid convergence of optical innovation and machine intelligence is reshaping biological imaging by enabling platforms that jointly advance image formation and computational reconstruction for highspeed, high-resolution volumetric microscopy. However, broadly accessible three-dimensional imaging at high spatiotemporal resolution remains limited by the reliance of existing supervised methods on large modality-matched training datasets, the computational burden of conventional iterative reconstruction, and sensitivity to optical mismatch arising from small deviations in the spatial-angular point spread functions. Here, we introduce HYPER-Net, a physics-conditioned self-supervised framework for Fourier light-field microscopy that integrates scan-free volumetric acquisition with fast, robust three-dimensional reconstruction. HYPER-Net incorporates experiment-specific point-spread functions into the learning process in two complementary roles: as the forward operator that enforces measurement consistency and as a conditioning signal that adaptively modulates intermediate feature representations. This design reduces reliance on paired experimental ground-truth volumes, improves robustness to system variation, and enables generalizable reconstruction across diverse biological contexts. Using human colon organoids, embryonic Xenopus laevis hearts, hiPSC-derived cardiac spheroids, and freely moving Caenorhabditis elegans, we demonstrate high-fidelity volumetric imaging of tissue morphology, cardiac function, calcium-contraction coupling, and locomotion-associated neural and muscular dynamics. These results position HYPER-Net as a versatile framework for rapid volumetric imaging and quantitative analysis of dynamic biological systems across basic research and biomedical applications.

bioengineering↗

bioRxiv2024

CandyCollect: An Open-Microfluidic Device for the Direct Capture and Enumeration of Salivary-Extracellular Vesicles

Extracellular vesicles (EVs) are promising biomarkers for disease detection using a liquid biopsy approach, in which they are enriched and analyzed directly from biofluids. However, implementing EV biomarker technologies in the clinic remains limited by the need for practical and patient-centric biofluid collection methods that are compatible with downstream EV processing and analysis. While saliva offers a non-invasive source of EVs, its complexity and heterogeneity--cells, debris, and other non-EV proteins--can present hurdles when using traditional analytical platforms. Here, we present the CandyCollect, a lollipop-inspired sampling device with open microfluidic channels, as a patient-friendly approach for rapid salivary EV capture. CandyCollect simplifies sample preparation by effectively pre-concentrating EVs in oxygen-plasma treated open microfluidic channels. In this proof-of-principle study, we show that following a 3-5 minute-oral sampling period, EVs collected by the CandyCollect can be released with high purity within minutes and subsequently quantified and analyzed for cargo content. We observed consistent EV capture across repeated collections within individuals and expected variability across healthy participants. Additionally, single and pooled collections of EVs from a healthy participant resulted in a concordant protein profile. Overall, the CandyCollect is a new platform for rapid, non-invasive salivary EV collection and analysis for clinical diagnostics.

bioengineering↗

bioRxiv2024

Remote sensing for estimating genetic parameters of biomass accumulation and modeling stability of growth curves in alfalfa

Multi-spectral imaging (MSI) collection by unoccupied aerial vehicles (UAV) is an important tool to measure growth of forage crops. Information from estimated growth curves can be used to infer harvest biomass and to gain insights in the relationship of growth dynamics and harvest biomass stability across cuttings and years. In this study, we used MSI to evaluate Alfalfa (Medicago sativa L. subsp. sativa) to understand the longitudinal relationship between vegetative indices (VIs) and forage/biomass, as well as evaluation of irrigation treatments and genotype by environment interactions (GEI) of different alfalfa cultivars. Alfalfa is a widely cultivated perennial forage crop grown for high yield, nutritious forage quality for feed rations, tolerance to abiotic stress, and nitrogen fixation properties in crop rotations. The direct relationship between biomass and VIs such as Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red edge normalized difference vegetation index (NDRE), and Near infrared (NIR) provide a non-destructive and high throughput approach to measure biomass accumulation over subsequent alfalfa harvests. In this study, we aimed to estimate the genetic parameters of alfalfa VIs and utilize longitudinal modeling of VIs over growing seasons to identify potential relationships between stability in growth parameters and cultivar stability for alfalfa biomass yield across cuttings and years. We found VIs of GNDVI, NDRE, NDVI, NIR and simple ratios to be moderately heritable with median values for the field trial in Ithaca, NY to be 0.64, 0.56, 0.45, 0.45 and 0.40 respectively, Normal Irrigation (NI) trial in Leyendecker, NM to be 0.3967, 0.3813, 0.3751, 0.3239 and 0.3019 respectively, and Summer Irrigation Termination (SIT) trial in Leyendecker, NM to be of 0.11225, 0.1389, 0.1375, 0.2539 and 0.1343, respectively. Genetic correlations between NDVI and harvest biomass ranged from 0.52 - .99 in 2020 and 0.08 - .99 in 2021 in the NY trial. Genetic correlations for NI trial in NM for NDVI ranged from 0.72 - .98 in 2021 and SIT ranged from 0.34-1.0 in 2021. Genotype by genotype by interaction (GGE) biplots were used to differentiate between stable and unstable cultivars for locations NY and NM, and Random regression modeling approaches were used to estimate growth parameters for each cutting. Results showed high correspondence between stability in growth parameters and stability, or persistency, in harvest biomass across cuttings and years. In NM, the SIT trial showed more variation in growth curves due to stress conditions. The temporal growth curves derived from NDVI, NIR and Simple ratio were found to be the best phenotypic indices on studying the stability of growth parameters across different harvests. The strong correlation between VIs and biomass present opportunities for more efficient screening of cultivars, and the correlation between estimated growth parameters and harvest biomass suggest longitudinal modeling of VIs can provide insights into temporal factors influencing cultivar stability.

genetics↗

bioRxiv2022

The rhizodynamics robot: Automated imaging system for studying long-term dynamic root growth

The study of plant root growth in real time has been difficult to achieve in an automated, high-throughput, and systematic fashion. Dynamic imaging of plant roots is important in order to discover novel root growth behaviors and to deepen our understanding of how roots interact with their environments. We designed and implemented the Generating Rhizodynamic Observations Over Time (GROOT) robot, an automated, high-throughput imaging system that enables time-lapse imaging of 90 containers of plants and their roots growing in a clear gel medium over the duration of weeks to months. The system uses low-cost, widely available materials. As a proof of concept, we employed GROOT to collect images of orchid root growth of multiple species over six months. Beyond imaging plant roots, our system is highly customizable and can be used to collect time-lapse image data of different container sizes and configurations regardless of what is being imaged, making it applicable to many fields that require longitudinal time-lapse recording.

plant biology↗

bioRxiv2020

Larger active site in an ancestral hydroxynitrile lyase increases catalytically promiscuous esterase activity

Hydroxynitrile lyases (HNLs) belonging to the /{beta}-hydrolase-fold superfamily evolved from esterases approximately 100 million years ago. Reconstruction of an ancestral hydroxynitrile lyase in the /{beta}-hydrolase fold superfamily yielded a catalytically active hydroxynitrile lyase, HNL1. Several properties of HNL1 differ from the modern HNL from rubber tree (HbHNL). HNL1 favors larger substrates as compared to HbHNL, is two-fold more catalytically promiscuous for ester hydrolysis (p-nitrophenyl acetate) as compared to mandelonitrile cleavage, and resists irreversible heat inactivation to 35 {degrees}C higher than for HbHNL. We hypothesized that the x-ray crystal structure of HNL1 may reveal the molecular basis for the differences in these properties. The x-ray crystal structure solved to 1.96-[A] resolution shows the expected /{beta}-hydrolase fold, but a 60% larger active site as compared to HbHNL. This larger active site echoes its evolution from esterases since related esterase SABP2 from tobacco also has a 38% larger active site than HbHNL. The larger active site in HNL1 likely accounts for its ability to accept larger hydroxynitrile substrates. Site-directed mutagenesis of HbHNL to expand the active site increased its promiscuous esterase activity 50-fold, consistent with the larger active site in HNL1 being the primary cause of its promiscuous esterase activity. Urea-induced unfolding of HNL1 indicates that it unfolds less completely than HbHNL (m-value = 0.63 for HNL1 vs 0.93 kcal/ mol{middle dot}M for HbHNL), which may account for the ability of HNL1 to better resist irreversible inactivation upon heating. The structure of HNL1 shows changes in hydrogen bond networks that may stabilize regions of the folded structure.

biochemistry↗