Monitoring underwater volcano degassing using fiber-optic sensing.

Continuous monitoring of volcanic gas emissions is crucial for understanding volcanic activity and potential eruptions. However, emissions of volcanic gases underwater are infrequently studied or quantified. This study explores the potential of Distributed Acoustic Sensing (DAS) technology to monitor underwater volcanic degassing. DAS converts fiber-optic cables into high-resolution vibration recording arrays, providing measurements at unprecedented spatio-temporal resolution. We conducted an experiment at Laacher See volcano in Germany, immersing a fiber-optic cable in the lake and interrogating it with a DAS system. We detected and analyzed numerous acoustic signals that we associated with bubble emissions in different lake areas. Three types of text-book bubbles exhibiting characteristic waveforms are all found from our detections, indicating different nucleation processes and bubble sizes. Using clustering algorithms, we classified bubble events into four distinct clusters based on their temporal and spectral characteristics. The temporal distribution of the events provided insights into the evolution of gas seepage patterns. This technology has the potential to revolutionize underwater degassing monitoring and provide valuable information for studying volcanic processes and estimating gas emissions. Furthermore, DAS can be applied to other applications, such as monitoring underwater carbon capture and storage operations or methane leaks associated with climate change.

Chimeric TK-NOG mice: a predictive model for cholestatic human liver toxicity.

Due to the substantial interspecies differences in drug metabolism and disposition, drug-induced liver injury (DILI) in humans is often not predicted by studies performed in animal species. For example, a drug (bosentan) used to treat pulmonary artery hypertension caused unexpected cholestatic liver toxicity in humans, which was not predicted by preclinical toxicology studies in multiple animal species. In this study, we demonstrate that NOG mice expressing a thymidine kinase transgene (TK-NOG) with humanized livers have a humanized profile of biliary excretion of a test (cefmetazole) drug, which was shown by an in situ perfusion study to result from interspecies differences in the rate of biliary transport and in liver retention of this drug. We also found that readily detectable cholestatic liver injury develops in TK-NOG mice with humanized livers after 1 week of treatment with bosentan (160, 32, or 6 mg/kg per day by mouth), whereas liver toxicity did not develop in control mice after 1 month of treatment. The laboratory and histologic features of bosentan-induced liver toxicity in humanized mice mirrored that of human subjects. Because DILI has become a significant public health problem, drug safety could be improved if preclinical toxicology studies were performed using humanized TK-NOG.

Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts.

BACKGROUND: We aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort. METHODS: We build a fully automated system for Word Sense Disambiguation by designing a system that does not require manually-constructed external resources or manually-labeled training examples except for a single ambiguous word. The system uses a novel and efficient graph-based algorithm to cluster words into groups that have the same meaning. Our algorithm follows the principle of finding a maximum margin between clusters, determining a split of the data that maximizes the minimum distance between pairs of data points belonging to two different clusters. RESULTS: On a test set of 21 ambiguous keywords from PubMed abstracts, our system has an average accuracy of 78%, outperforming a state-of-the-art unsupervised system by 2% and a baseline technique by 23%. On a standard data set from the National Library of Medicine, our system outperforms the baseline by 6% and comes within 5% of the accuracy of a supervised system. CONCLUSION: Our system is a novel, state-of-the-art technique for efficiently finding word sense clusters, and does not require training data or human effort for each new word to be disambiguated.