Biology subjects

Stone, K.

Publications and source records attributed to Stone, K..

4 recordsLinked to original sources

bioRxiv2026

Multiscale learning and topological analysis across complex postures enable robust nematode size quantification in pharmacological assays

Body size is an important trait that reflects animal development and physiology. In nematodes, precise measurement is valuable for linking variation in body dimensions to biological questions such as developmental timing, genetic regulation, and drug responses. However, robust size measurements can be difficult to obtain because nematodes can vary in curvature, have self-intersecting postures, and overlap with neighboring animals. Current image analysis software, such as CellProfiler, can measure isolated animals in straight postures but struggles with curly or overlapping animals. Here, we present NemaSize, an artificial intelligence (AI)-aided pipeline to measure Caenorhabditis nematode body sizes across complex postures using multi-scale learning and topology-aware skeletonization. Using You Only Look Once (YOLO) models trained at different spatial scales, NemaSize first identifies individual animals in a large field of view (FOV; 6.75 x 6.75 mm) and then performs high-resolution body segmentation in the region of interest (ROI). Next, NemaSize converts segmented body masks into topological graph representations, allowing curly and overlapping animals to be classified and skeletonized according to the body topology. NemaSize achieved less than 4% overall error in length and width measurements across all posture classes. Compared to CellProfiler, NemaSize demonstrated higher robustness for complex postures, including a 48% error reduction in length measurements for curly or self-overlapping animals. Application of NemaSize in high-throughput imaging assays further shows that NemaSize provides accurate quantification of Caenorhabditis briggsae larval development in response to the anthelmintic drug ivermectin, a task that was difficult for CellProfiler because of curly animal postures. Together, NemaSize provides a robust approach for automated body size quantification for Caenorhabditis nematodes and will support broad applications in high-throughput pharmacological and genetic screens. Beyond nematodes, NemaSize introduces a multiscale computational framework for analyzing elongated biological objects with complex topologies.

pharmacology and toxicology↗

bioRxiv2023

Nighthawk: acoustic monitoring of nocturnal bird migration in the Americas

O_LIAnimal migration is one of natures most spectacular phenomena, but migratory animals and their journeys are imperiled across the globe. Migratory birds are among the most well-studied animals on Earth, yet relatively little is known about in-flight behavior during nocturnal migration. Because many migrating bird species vocalize during flight, passive acoustic monitoring shows great promise for facilitating widespread monitoring of bird migration. C_LIO_LIHere, we present Nighthawk, a deep learning model designed to detect and identify the vocalizations of nocturnally migrating birds. We trained Nighthawk on the in-flight vocalizations of migratory birds using a diverse dataset of recordings from across the Americas. C_LIO_LIOur results demonstrate that Nighthawk performs well as a nocturnal flight call detector and classifier for dozens of avian taxa, both at the species level and for broader taxonomic groups (e.g., orders and families). The model accurately quantified nightly nocturnal migration intensity and species phenology and performed well on data from across North America. Incorporating modest amounts of additional annotated audio (50-120 h) into model training yielded high performance on target datasets from both North and South America. C_LIO_LIBy monitoring the vocalizations of actively migrating birds, Nighthawk provides a detailed window onto nocturnal bird migration that is not presently attainable by other means (e.g., radar or citizen science). Scientists, managers, and practitioners could use acoustic monitoring with Nighthawk for a number of applications, including: monitoring migration passage at wind farms; studying airspace usage during migratory flights; monitoring the changing migrations of species susceptible to climate change; and revealing previously unknown migration routes and behaviors. Overall, this work will empower diverse stakeholders to efficiently monitor migrating birds across the Western Hemisphere and collect data in aid of science and conservation. Nighthawk is freely available at https://github.com/bmvandoren/Nighthawk. C_LI

ecology↗

bioRxiv2023

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross- validated regularized regression to link sleep EEG features to cognitive performance in cross- sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics accounted for more covariance between sleep EEG and cognition than health variables, and consequently reduced this association by a greater degree, but even with the strictest covariate sets a statistically significant association was present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r=0.283), with the strongest effect ascribed to spindle- frequency activity. This association becomes weaker after adjusting for demographic (r=0.186) and health variables (r=0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

neuroscience↗

bioRxiv2017

Exome sequencing and genotyping identify a rare variant in NLRP7 gene associated with ulcerative colitis.

Background and aimsAlthough genome-wide association studies (GWAS) in inflammatory bowel disease (IBD) have identified a large number of common disease susceptibility alleles for both Crohns disease (CD) and ulcerative colitis (UC), a substantial fraction of IBD heritability remains unexplained, suggesting that rare coding genetic variants may also have a role in pathogenesis. We used high-throughput sequencing in families with multiple cases of IBD, followed by genotyping of cases and controls, to investigate whether rare protein altering genetic variants are associated with susceptibility to IBD.\n\nMethodsWhole exome sequencing was carried out in 10 families in which 3 or more individuals were affected with IBD. A stepwise filtering approach was applied to exome variants to identify potential causal variants. Follow-up genotyping was performed in 6,025 IBD cases (2,948 CD; 3,077 UC) and 7,238 controls.\n\nResultsOur exome variant analysis revealed coding variants in the NLRP7 gene that were present in affected individuals in two distinct families. Genotyping of the two variants, p.S361L and p.R801H, in IBD cases and controls showed that the p.S361L variant was significantly associated with an increased risk of ulcerative colitis (odds ratio 4.79, p=0.0039) and IBD (odds ratio 3.17, p=0.037). A combined analysis of both variants showed suggestive association with an increased risk of IBD (odds ratio 2.77, p=0.018).\n\nConclusionsThe results suggest that NLRP7 signalling and inflammasome formation may be a significant component in the pathogenesis of IBD.

genetics↗