Biology subjects

Newell, J.

Publications and source records attributed to Newell, J..

4 recordsLinked to original sources

bioRxiv2026

Ecological context shapes the interpretation of biodiversity intactness metrics

Developing biodiversity metrics that are both operational for decision-making and ecologically meaningful remains challenging. Mean Species Abundance (MSA) is widely used to quantify biodiversity intactness in applications including biodiversity scenario modeling, spatial assessment, policy and planning, and life cycle assessment (LCA). MSA compares species abundance under disturbed conditions with their abundance under a reference condition, but the pressure-impact relationships used to calculate it are generalized across studies and may not adequately represent variation among ecological contexts or taxonomic groups. This is particularly relevant when MSA is transferred across regions or used to support comparisons and environmental decision-making. We compiled biodiversity data from 7,064 forest fragments containing 21,761 species of arthropods, amphibians, birds, mammals, squamates, and plants across 11 biomes worldwide. We evaluated how MSA relates to broader ecological outcomes, including local and regional species richness and ecosystem productivity, and whether these relationships vary across ecological contexts (vegetation condition, landscape structure, and biome) and taxonomic groups. MSA was positively associated with local species richness and ecosystem productivity across all sites, but these associations weakened, disappeared, or reversed depending on ecological context and taxonomic group. For example, the MSA-productivity association held in only five of 11 biomes, and the MSA-regional richness association was negative in temperate conifer and boreal forests but positive in four other biomes. We propose a simple decision guide linking assessment goals, contextual evaluation, and interpretation to help identify when MSA can be used directly, when ecological context should guide its interpretation, and when complementary ecological information is needed. These findings may extend to other biodiversity metrics that compare impacted conditions with reference states and apply generalized impact relationships across ecological contexts and taxonomic groups.

ecology↗

bioRxiv2026

Single Particle Adsorption and Response Quantification for Functional AAVx Titer and Dose Analysis

Accurate quantification of functional adeno-associated virus (AAV) dose remains a critical limitation in gene therapy, where titers are defined using ensemble measurements of viral genomes or capsid concentration that average across heterogeneous populations and weakly predict transduction. We present single-particle adsorption and response quantification (SPARQ), a diffraction-limited imaging platform for quantifying AAVx vectors, regardless of serotype, at single-nanoparticle and single-cell resolution. SPARQ immobilizes and resolves individual AAVx, enabling concurrent measurement of capsid-associated and genome-associated signals before cell exposure. Across multiple serotypes, including AAV2, AAV5, and AAVrh74, SPARQ identifies full-capsid fractions, consistent with bulk measurements obtained by ddPCR/ELISA, DLS/UV-Vis, charge detection, and mass photometry, while reporting narrower distributions than ensemble-derived values and revealing systematic discrepancies in bulk-derived titers arising from population averaging. Surface immobilization is achieved by charge-dependent AAVx-surface interactions, as supported by multiphysics simulations, which capture serotype-dependent adsorption kinetics governed by capsid electrostatics for AAV2, AAV5, AAV6, and AAV9. Furthermore, SPARQ directly measures transduction as a function of particle number and reveals a threshold response. Transduction efficiency increased from [~]29% (S.D {+/-} 3.31%) at [~]18 (S.D {+/-} 7.55) AAVs per cell to [~]66% (S.D {+/-} 1.66%) at [~]42 (S.D {+/-} 9.69) AAVs per cell, which is comparable with conventional in vitro assay requiring 105 AAVs/cell. Cumulative fluorescence measurements across large micro-patterned arrays of single cells recapitulate bulk-like high-throughput scaling, while SPARQs single-nanoparticle resolution reveals functional heterogeneity that is masked by population averaged assays. SPARQ provides a particle-resolved framework for functional AAVx titration and quantitative characterization, enabling the development of quality control and dosing strategies in gene therapy manufacturing. TeaserSPARQ combines light-activated surface viral adsorption with diffraction-limited imaging to directly quantify AAV integrity, heterogeneity, and functional transduction at the single-particle level.

bioengineering↗

bioRxiv2020

A 3D Reconstruction Algorithm for Real-time Simultaneous Multi-Source EIT Imaging for Lung Function Monitoring

Monitoring regional pulmonary ventilation and pulsatile perfusion changes in a 3D region of interest (ROI) of the lung is a promising application for electrical impedance tomography (EIT). This paper describes a 3D analytical reconstruction algorithm that was embedded in a prototype EIT system to enable a real-time image reconstruction at nearly 20 frames per second for monitoring impedance changes in the chest in real-time. The derivation and results of the 3D analytical forward solution and inverse solution and details of the real-time reconstruction algorithm are given. The algorithm and EIT system are validated with simulated data, in-vitro phantoms, and finally shown to be capable of imaging ventilation and pulsatile perfusion in human subjects. The human subject data was obtained using a high-precision, high-speed and simultaneous multiple current source (SMS-EIT) developed by GE Research. Data was collected using four rows of 8 electrodes for a healthy adult male subject and 2 rows of 16 electrodes for six healthy human female subjects, with one row placed above the breasts and a second row placed at the infra-mammary fold. Each of the female subjects performed a breathing maneuver with a volumetric incentive spirometer, and the volume of air inhaled was calculated from the EIT images. Pulsatile perfusion images were computed from this data, and regional lung filling was also analyzed.

bioengineering↗

bioRxiv2018

Joint analysis of matched tumor samples with varying tumor contents improves somatic variant calling in the absence of a germline sample

Archival tumor samples represent a potential rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and germline variants that are private to the individual. Archival sections often contain adjacent normal tissue, but this normal tissue can include infiltrating tumor cells. Comparative somatic variant callers are designed to exclude variants present in the normal sample, so a novel approach is required to leverage sequencing of adjacent normal tissue for somatic variant calling. Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient. The approach is based on the concept that the allelic fraction of somatic variants, but not germline variants, would be reduced in samples with low tumor content. LumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses the model to determine expected allelic fractions for somatic and germline variants and classify variants accordingly. To evaluate using LumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high tumor content, low tumor content, and germline exome sequencing data (to define true somatic variants) available for each patient. We show that both sensitivity and positive predictive value are improved by analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or compared to in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which normal samples were not available for germline sequencing, but tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using LumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent normal as a reference. Together, these results demonstrate the potential utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional DNA sample is not available.

bioinformatics↗