Engineering topics

Lagergren, John H.

Publications and source records attributed to Lagergren, John H..

4 records

DOE OSTI2026

APPL Hyperspectral_Imaging_Dataset_for_Heritability_Analysis_in_Populus_trichocarpa

This dataset contains hyperspectral imaging data collected at the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory. Natural variants of Populus trichocarpa were imaged using a high-throughput hyperspectral phenotyping pipeline to quantify spectral reflectance traits for downstream quantitative genetics analyses. The dataset includes hyperspectral image files and derived reflectance data products suitable for extracting spectral features across the measured wavelength range (e.g., VNIR and/or SWIR, depending on instrument configuration), along with associated sample metadata (e.g., genotype identifiers, experimental design factors, and imaging run identifiers). These data were generated to support analyses of broad-sense heritability of hyperspectral traits and their relationships with biochemical phenotypes (including lignin traits from Py-MBMS).

APPL↗

DOE OSTI2026

Pyrolysis Molecular Beam Mass Spectrometry_Analysis_of_Natural_Variants_of_Poplulus_Trichocarpa_Leaves

Select leaves from natural variants of Poplar (Populus Trichocarpa) grown in a greenhouse at Oak Ridge National Laboratory were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Leaves were harvested, cryomilled and kept frozen until analysis. Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗

DOE OSTI2026

Genome_shuffling_enables_quantitative_trait_locus_mapping_in_Bacillus_subtilis

Genetic mapping is a powerful tool for eukaryotic genetics that has only been applied to bacteria in limited circumstances. Quantitative trait locus (QTL) mapping generally relies on sexual recombination to break linkages between genes, yet bacteria rarely undergo sufficient homologous recombination to generate suitable mapping populations. In this work, we used iterative biparental genome shuffling by protoplast fusion inBacillus subtilisto generate a population of bacteria with substantial random recombination throughout their genomes. Individual shuffled progeny were arrayed in well plates, resequenced, and characterized for a range of complex phenotypes including spore germination and swarming motility. Genetic mapping of the resulting phenotypes identified high-confidence QTLs of moderate size (∼10 kb), and these associations were validated through targeted genetic swaps. ThisB. subtilisQTL population can easily be used to map additional phenotypes, and the general approach for QTL mapping is applicable in a wide range of bacteria.

Bacillus subtilis↗

DOE OSTI2024

Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

59 BASIC BIOLOGICAL SCIENCES↗