Biology subjects

Wee, J.

Publications and source records attributed to Wee, J..

3 recordsLinked to original sources

bioRxiv2026

Deep learning-based counting and body-area measurement enable sensitive population-level endpoints in the Collembola reproduction test

Springtails (Collembola) are standard test organisms for soil ecotoxicological assessment; under the ISO 11267 and OECD 232 guidelines, adult abundance and juvenile production are the primary endpoints. However, these rely on labor-intensive manual counting and capture only abundance, overlooking sublethal effects on growth. We developed an automated system that couples YOLOv8 object detection with Voronoi-Otsu segmentation-based body-area measurement, so that adults and juveniles of Allonychiurus kimi are counted and their growth (body area) measured simultaneously, and applied it to toxicity assessment based on average and cumulative body area and to cohort analysis of juvenile body-area distributions using a Gaussian mixture model (GMM). The model was trained on 142 annotated images and validated on 160 images from toxicity tests with cadmium (Cd), arsenic (As), and copper (Cu), and both detection accuracy and agreement with manual measurements were excellent (coefficient of determination, R2 >= 0.95). Cumulative body area, a composite indicator integrating abundance and individual size and thus reflecting both reproduction and growth, showed the lowest EC50 in all three metal(loid)s (Cd 14.10, As 10.53, Cu 398.80 mg kg-1). Cohort analysis showed that the two cohorts present in the controls merged into one at the highest concentrations (As 20, Cu 800 mg kg-1), indicating that toxic stress drives not only individual-level suppression of reproduction and growth but also population restructuring. Beyond accurately and efficiently replacing manual counting, the system adds more sensitive composite endpoints and population-level analysis, providing a more comprehensive, scalable framework for soil ecotoxicological risk assessment.

ecology↗

bioRxiv2024

A proposed framework for evaluating meat alternatives

Concerns surrounding the environmental, economic, and ethical consequences of meat production and industrial agriculture have prompted substantial research and capital investment into the production of meat alternatives. Alternative meat production encompasses a variety of technological approaches including plant-based meats, cell-based or cultivated meats, meat alternatives relying on fungal protein sources, and hybrids thereof; each of which offers unique advantages and disadvantages and has been associated with a myriad of claims supporting it as the preferred alternative to animal-derived meats. As part of XPRIZE Foundations Feed the Next Billion competition, we developed a framework for evaluating meat alternatives by measuring their structural, nutritional, and organoleptic properties while also assessing safety and their purported environmental and economic benefits compared to animal-derived meats. The framework is technologically agnostic and can be used to evaluate meat alternatives of all types. The output of the framework enables a data-driven comparison to animal-derived meat and/or other alternative meats, allowing a range of stakeholders (e.g., food startups, investors, government) to assess technological readiness, competitive advantage, and impact potential. This framework can assist this nascent industry as it moves towards standardizing approaches to evaluating the quality, safety and proposed benefits of meat alternatives.

bioengineering↗

bioRxiv2023

Multi-pass, single-molecule nanopore reading of long protein strands with single-amino acid sensitivity

The ability to sequence single protein molecules in their native, full-length form would enable a more comprehensive understanding of proteomic diversity. Current technologies, however, are limited in achieving this goal. Here, we establish a method for long-range, single-molecule reading of intact protein strands on a commercial nanopore sensor array. By using the ClpX unfoldase to ratchet proteins through a CsgG nanopore, we achieve single-amino acid level sensitivity, enabling sequencing of combinations of amino acid substitutions across long protein strands. For greater sequencing accuracy, we demonstrate the ability to reread individual protein molecules, spanning hundreds of amino acids in length, multiple times, and explore the potential for high accuracy protein barcode sequencing. Further, we develop a biophysical model that can simulate raw nanopore signals a priori, based on amino acid volume and charge, enhancing the interpretation of raw signal data. Finally, we apply these methods to examine intact, folded protein domains for complete end-to-end analysis. These results provide proof-of-concept for a platform that has the potential to identify and characterize full-length proteoforms at single-molecule resolution.

biophysics↗