Background: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to...
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Background: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches. Results: We propose a consensus methodbased on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem. Conclusions: The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.
Globally, mangroves have been promoted to protect the coastal ecosystems and human settlements against weather vagaries including climate change impacts. However, climate change can also affect the mangrove ecosystems...
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Globally, mangroves have been promoted to protect the coastal ecosystems and human settlements against weather vagaries including climate change impacts. However, climate change can also affect the mangrove ecosystems, affecting their ability to mitigate losses and damages caused by climate change. Recognizing the need to understand the impact of climate change on the ability of mangroves to mitigate loss and damage, this paper presents the impact of climate change on mangrove ecosystems in Dat Mui commune, Ngoc Hien district, Ca Mau province, Vietnam by using community-based methods. Results showed that the most noticeable impact of climate change is the loss in mangrove area, aquatic resources, and coastal erosion prevention. In addition, there is a decline in timber, firewood supply, and habitat of mangrove species. Despite adaptation actions taken by the local authorities and households, mangrove ecosystems are is still facing loss and damage. Solutions have been proposed to help the local and national authorities and communities to address losses and damages caused by the climate change.
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