Russ, J. | Search bioRxiv

bioRxiv2026

Systemic hypoxia drives glycogen-fueled progression of lung adenocarcinoma

In advanced stages, lung adenocarcinoma obstructs airways and disrupts ventilation-perfusion relationships in the lung, causing systemic hypoxemia and enabling a feed-forward loop that accelerates malignancy. Systemic hypoxemia is also experienced due to common respiratory comorbidities such as chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA), potentially accelerating malignancy. In a statewide electronic health record network, pre-existing COPD (598 matched pairs) or sleep apnea (235 matched pairs) independently predicted worse survival following incident lung cancer diagnosis. Since the mechanistic basis of the link between malignancy and hypoxia is not well understood, we created systemic hypoxia in KrasLSL-G12D/+;Trp53fl/fl (KP) mice by delivering low inspired oxygen concentrations (8% inspired oxygen; 8 h daily). Hypoxia nearly doubled tumor multiplicity and selectively remodeled cancer central carbon metabolism. Spatially resolved metabolomics revealed marked tumor-compartment glycogen accumulation, elevated tricarboxylic-acid cycle intermediates, and depleted glycolytic pools. Quantitative proteomics across cellular models and autochthonous tumors demonstrated that systemic hypoxia drives glycogen mobilization selectively through the lysosomal enzyme acid -glucosidase (GAA). Tumor-cell-autonomous deletion of GAA eliminated the hypoxia-driven growth advantage and disrupted downstream anabolic biosynthetic pathways. Thus, systemic hypoxia drives lung adenocarcinoma expansion by mobilizing lysosomal glycogen reserves through GAA to sustain proliferative growth.

cancer biology↗

bioRxiv2017

Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection

O_LIPassive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings.\nC_LIO_LIWe developed a convolutional neural network (CNN) based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats (BatDetect). Our deep learning algorithms (CNN FULL and CNN FAST) were trained on full-spectrum ultrasonic audio collected along road-transects across Romania and Bulgaria by citizen scientists as part of the iBats programme and labelled by users of www.batdetective.org. We compared the performance of our system to other algorithms and commercial systems on expert verified test datasets recorded from different sensors and countries. As an example application, we ran our detection pipeline on iBats monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system.\nC_LIO_LIHere, we show that both CNNFULL and CNNFAST deep learning algorithms have a higher detection performance (average precision, and recall) of search-phase echolocation calls with our test sets, when compared to other existing algorithms and commercial systems tested. Precision scores for commercial systems were reasonably good across all test datasets (>0.7), but this was at the expense of recall rates. In particular, our deep learning approaches were better at detecting calls in road-transect data, which contained more noisy recordings. Our comparison of CNNFULL and CNNFAST algorithms was favourable, although CNNFAST had a slightly poorer performance, displaying a trade-off between speed and accuracy. Our example monitoring application demonstrated that our open-source, fully automatic, BatDetect CNNFAST pipeline does as well or better compared to a commercial system with manual verification previously used to analyse monitoring data.\nC_LIO_LIWe show that it is possible to both accurately and automatically detect bat search-phase echolocation calls, particularly from noisy audio recordings. Our detection pipeline enables the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale, particularly when combined with automatic species identification. We release our system and datasets to encourage future progress and transparency.\nC_LI

ecology↗