2026-10-11T01:33:47Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33867712 2026-09-16T17:54:14Z category_4 category_146 category_272 category_873 category_734 category_931 category_69 portal_5 item_type_3 month_year_09_2026

<p>GPS coordinates of sampling stations.</p> Irène Godéré (24998959) Taiamiti Edmunds (24998962) Nabila Gaertner-Mazouni (713602) Fiona Gimenez (24998965) Pascal Wong-Wah-Chung (8604789) Stéphanie Lebarillier (11911045) Magalie Baudrimont (1461805) Chloé Pupier (24998968) Nicolas Maihota (24998971) Jean-Claude Gaertner (430345) Biochemistry Space Science Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Inorganic Chemistry throughput image processing sup >&# 8722 segment fluorescent particles reproducible sample analysis reducing blooming artifacts face unique challenges exploring nile red div >< p combining nile red 9 – 52 5 – 11 1 </ sup refine fluorescence thresholds 9 %), pvc enhance polymer detection µftir validation revealed tridacna maxima </ 6711 fluorescence images giant clam samples model achieved f1 80 ± 0 maxima </ giant clam triband fluorescence net model fluorescence staining composite images 6 %), learning detection giant clams µftir spectroscopy wet weight tritc ), suggesting contributions monitoring due methodological standardization machine learning limited infrastructure insular systems human annotation household waste highlighting areas highest concentrations guided multi future work <div><p>Small Island Developing States (SIDS) face unique challenges for microplastics (MPs) monitoring due to limited infrastructure and resources. In this context, we propose and test innovative approaches toward a standardized, low-cost methodology for quantifying MPs in SIDS. We evaluate the giant clam <i>T. maxima</i> as a bio-integrator, combining Nile red (NR) fluorescence staining with automated machine-learning detection. We optimized a digestion protocol using KOH and HNO₃ for <i>T. maxima</i> viscera, and developed a DAPI-guided multi-spectra composite imaging approach based on triband fluorescence (DAPI, FITC, TRITC), to enhance polymer detection while reducing blooming artifacts. A semi-automated annotation pipeline using Labkit interactive segmentation with CLIP/UMAP clustering efficiently generated training data from 6711 fluorescence images. A U-Net model was trained on composite images to segment fluorescent particles. The workflow was applied to giant clams from three French Polynesian islands (Makemo, Hao, Tubuai), and NR-based estimates were validated against µFTIR spectroscopy. The model achieved F1-scores of 0.741 for giant clam samples and 0.657 for controls, comparable to human annotation (F1 = 0.680). MPs were detected across all islands, with highest concentrations in gills (16.9–52.7 particles·g⁻¹ wet weight) compared to viscera (2.5–11.0 particles·g⁻¹ ww). µFTIR validation revealed that NR overestimates MP counts (µFTIR: 0.80 ± 0.16 particles·g⁻¹ ww at Tubuai), primarily due to false positives from proteins, cellulose, and stearates. In Tubuai, polyamide (28.9%), PVC (12.6%), and polystyrene (10.7%) were the dominant polymers, suggesting contributions from fishing gear, agriculture, and household waste. While NR-based quantification overestimates absolute MP counts, the automated pipeline demonstrates potential for high-throughput image processing, reproducible sample analysis, and methodological standardization. This workflow represents a first step toward scalable, low-cost approaches for MPs monitoring in insular systems, highlighting areas for further calibration and optimization. Future work should refine fluorescence thresholds and expand validation across species and locations.</p></div> 2026-09-16T17:53:46Z Dataset Dataset 10.1371/journal.pone.0357014.t001 https://figshare.com/articles/dataset/_p_GPS_coordinates_of_sampling_stations_p_/33867712 CC BY 4.0