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Abstract Biological dinitrogen (N2) fixation supplies nitrogen to the oceans, supporting primary productivity, and is carried out by some bacteria and archaea referred to as diazotrophs. Cyanobacteria are conventionally considered to be the major contributors to marine N2 fixation, but non-cyanobacterial diazotrophs (NCDs) have been shown to be distributed throughout ocean ecosystems. However, the biogeochemical significance of marine NCDs has not been demonstrated. This review synthesizes multiple datasets, drawing from cultivation-independent molecular techniques and data from extensive oceanic expeditions, to provide a comprehensive view into the diversity, biogeography, ecophysiology, and activity of marine NCDs. A NCD nifH gene catalog was compiled containing sequences from both PCR-based and PCR-free methods, identifying taxa for future studies. NCD abundances from a novel database of NCD nifH-based abundances were colocalized with environmental data, unveiling distinct distributions and environmental drivers of individual taxa. Mechanisms that NCDs may use to fuel and regulate N2 fixation in response to oxygen and fixed nitrogen availability are discussed, based on a metabolic analysis of recently available Tara Oceans expedition data. The integration of multiple datasets provides a new perspective that enhances understanding of the biology, ecology, and biogeography of marine NCDs and provides tools and directions for future research.
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This content will become publicly available on December 1, 2025
DiseaseNet: a transfer learning approach to noncommunicable disease classification
Abstract As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs.
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- Award ID(s):
- 2051062
- PAR ID:
- 10518660
- Publisher / Repository:
- BMC
- Date Published:
- Journal Name:
- BMC Bioinformatics
- Volume:
- 25
- Issue:
- 1
- ISSN:
- 1471-2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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