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Persistent eutrophication and hypoxia in the coastal ocean
- Minhan Dai, Yangyang Zhao, Fei Chai, Mingru Chen, Nengwang Chen, Yimin Chen, Danyang Cheng, Jianping Gan, Dabo Guan, Yuanyuan Hong, Jialu Huang, Yanting Lee, Kenneth Mei Yee Leung, Phaik Eem Lim, Senjie Lin, Xin Lin, Xin Liu, Zhiqiang Liu, Ya-Wei Luo, Feifei Meng, Chalermrat Sangmanee, Yuan Shen, Khanittha Uthaipan, Wan Izatul Asma Wan Talaat, Xianhui Sean Wan, Cong Wang, Dazhi Wang, Guizhi Wang, Shanlin Wang, Yanmin Wang, Yuntao Wang, Zhe Wang, Zhixuan Wang, Yanping Xu, Jin-Yu Terence Yang, Yan Yang, Moriaki Yasuhara, Dan Yu, Jianmin Yu, Liuqian Yu, Zengkai Zhang, Zhouling Zhang
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- Journal:
- Cambridge Prisms: Coastal Futures / Volume 1 / 2023
- Published online by Cambridge University Press:
- 23 February 2023, e19
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- Article
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- You have access Access
- Open access
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Coastal eutrophication and hypoxia remain a persistent environmental crisis despite the great efforts to reduce nutrient loading and mitigate associated environmental damages. Symptoms of this crisis have appeared to spread rapidly, reaching developing countries in Asia with emergences in Southern America and Africa. The pace of changes and the underlying drivers remain not so clear. To address the gap, we review the up-to-date status and mechanisms of eutrophication and hypoxia in global coastal oceans, upon which we examine the trajectories of changes over the 40 years or longer in six model coastal systems with varying socio-economic development statuses and different levels and histories of eutrophication. Although these coastal systems share common features of eutrophication, site-specific characteristics are also substantial, depending on the regional environmental setting and level of social-economic development along with policy implementation and management. Nevertheless, ecosystem recovery generally needs greater reduction in pressures compared to that initiated degradation and becomes less feasible to achieve past norms with a longer time anthropogenic pressures on the ecosystems. While the qualitative causality between drivers and consequences is well established, quantitative attribution of these drivers to eutrophication and hypoxia remains difficult especially when we consider the social economic drivers because the changes in coastal ecosystems are subject to multiple influences and the cause–effect relationship is often non-linear. Such relationships are further complicated by climate changes that have been accelerating over the past few decades. The knowledge gaps that limit our quantitative and mechanistic understanding of the human-coastal ocean nexus are identified, which is essential for science-based policy making. Recognizing lessons from past management practices, we advocate for a better, more efficient indexing system of coastal eutrophication and an advanced regional earth system modeling framework with optimal modules of human dimensions to facilitate the development and evaluation of effective policy and restoration actions.
13 - Inference of gene networks associated with the host response to infectious disease
- from Part IV - Big data over biological networks
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- By Zhe Gan, Duke University, USA, Xin Yuan, Duke University, USA, Ricardo Henao, Duke University, USA, Ephraim L. Tsalik, Duke University Medical Center, USA, Lawrence Carin, Duke University, USA
- Edited by Shuguang Cui, Texas A & M University, Alfred O. Hero, III, University of Michigan, Ann Arbor, Zhi-Quan Luo, University of Minnesota, José M. F. Moura, Carnegie Mellon University, Pennsylvania
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- Book:
- Big Data over Networks
- Published online:
- 18 December 2015
- Print publication:
- 14 January 2016, pp 365-390
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Summary
Inspired by the problem of inferring gene networks associated with the host response to infectious diseases, a new framework for discriminative factor models is developed. Bayesian shrinkage priors are employed to impose (near) sparsity on the factor loadings, while non-parametric techniques are utilized to infer the number of factors needed to represent the data. Two discriminative Bayesian loss functions are investigated, i.e. the logistic log-loss and the max-margin hinge loss. Efficient mean-field variational Bayesian inference and Gibbs sampling are implemented. To address large-scale datasets, an online version of variational Bayes is also developed. Experimental results on two real world microarray-based gene expression datasets show that the proposed framework achieves comparatively superior classification performance, with model interpretation delivered via pathway association analysis.
Background
From a statistical-modeling perspective, gene expression analysis can be roughly divided into two phases: exploration and prediction. In the former, the practitioner attempts to get a general understanding of a dataset by modeling its variability in an interpretable way, such that the inferred model can serve as a feature extractor and hypotheses generating mechanism of the underlying biological processes. Factor models are among the most widely employed techniques for exploratory gene expression analysis [1, 2], with principal component analysis a popular special case [3]. Predictive modeling, on the other hand, is concerned with finding a relationship between gene expression and phenotypes, that can be generalized to unseen samples. Examples of predictive models include classification methods like logistic regression and support vector machines [4, 5].
Factor models infer a latent covariance structure among the genes or biomarkers, with data modeled as generated from a noisy low-rank matrix factorization, manifested in terms of a loadings matrix and a factor scores matrix. Different specifications for these matrices give rise to special cases of factor models, such as principal components analysis [6], nonnegative matrix factorization [7], independent component analysis [8], and sparse factor models [1]. Factor models employing a sparse factor loadings matrix are of significant interest in gene-expression analysis, as the nonzero elements in the loadings matrix may be interpreted as correlated gene networks [1, 2, 9].