分类： 生物学 >> 生态学 提交时间： 2023-06-01
Fu, Hui
Ge, Yili
Cai, Guojun
Deng, Jianming
Liu, Huanyao
Wu, Aiping
Li, Youzhi
Li, Wei
Yuan, Guixiang
Erik Jeppesen
摘要： Understanding how phytoplankton interacts with local and regional drivers as well as their feedbacks is a great challenge, and quantitative analyses of the regulating role of human activities and climate changes on these feedback loops are also limited. By using monthly monitoring dataset (2000-2017) from Lake Taihu and empirical dynamic modelling to construct causal networks, we quantified the strengths of causal feedbacks among phytoplankton, local environments, zooplankton, meteorology as well as global climate oscillation. Prevalent bidirectional causal linkages between phytoplankton and the tested drivers were found, providing holistic and quantitative evidence of the ubiquitous feedback loops. Phytoplankton exhibited the highest feedbacks with total inorganic nitrogen and ammonia and the lowest with nitrate. The feedbacks between phytoplankton and environmental factors from 2000 to 2017 could be classified by two groups: the local environments (e.g., nutrients, pH, transparency, zooplankton)-driven enhancement loops promoting the response of the phytoplankton, and the climate (e.g., wind speed)-driven regulatory loops suppressing it. The two counterbalance groups modified the emergent macroecological patterns. Our findings revealed that the causal feedback networks loosened significantly after 2007 following nutrient loading reduction and unsuccessful biomanipulation restoration attempts by stocking carp. The strength of enhancement loops underwent marked decreases leading to reduced phytoplankton responses to the tested drivers, while the climate (decreasing wind speed, warming winter)-driven regulatory loops increased like a tug-of-war. To counteract the self-amplifying feedback loops, the present eutrophication mitigation efforts, especially nutrient reduction, should be continued, and introduction of alternative measures to indirectly regulate the critical components (e.g., pH, Secchi depth, zooplankton biomass) of the loops would be beneficial.
分类： 生物学 >> 生物物理学 提交时间： 2016-05-12
Liu, Mi
Wu, Aiping
Jiang, Taijiao
Liu, Mi
Wu, Aiping
Jiang, Taijiao
Liu, Mi
Wu, Aiping
Jiang, Taijiao
Liu, Mi
Hua, Sha
Wu, Aiping
Jiang, Taijiao
Zhao, Xiang
Li, Xiyan
Lan, Yu
Wang, Dayan
Shu, Yuelong
Hua, Sha
Du, Xiangjun
摘要： The influenza A (H1N1) virus causes seasonal epidemics that result in severe illnesses and deaths almost every year. A deep understanding of the antigenic patterns and evolution of human influenza A (H1N1) virus is extremely important for its effective surveillance and prevention. Through development of antigenicity inference method for human influenza A (H1N1), named PREDAC-H1, we systematically mapped the antigenic patterns and evolution of the human influenza A (H1N1) virus. Eight dominant antigenic clusters have been inferred for seasonal H1N1 viruses since 1977, which demonstrated sequential replacements over time with a similar pattern in Asia, Europe and North America. Among them, six clusters emerged first in Asia. As for China, three of the eight antigenic clusters were detected in South China earlier than in North China, indicating the leading role of South China in H1N1 transmission. The comprehensive view of the antigenic evolution of human influenza A (H1N1) virus can help formulate better strategy for its prevention and control.
分类： 生物学 >> 生物物理学 提交时间： 2016-05-12
摘要： Protein-protein interaction (PPI) networks serve as a powerful tool for unraveling protein functions, disease-gene and disease-disease associations. However, a direct strategy for integrating protein interaction, protein function and diseases is still absent. Moreover, the interrelated relationships among these three levels are poorly understood. Here we present a novel systematic method to integrate protein interaction, function, and disease networks. We first identified topological modules in human protein interaction data using the network topological algorithm (NeTA) we previously developed. The resulting modules were then associated with functional terms using Gene Ontology to obtain functional modules. Finally, disease modules were constructed by associating the modules with OMIM and GWAS. We found that most topological modules have cohesive structure, significant pathway annotations and good modularity. Most functional modules (70.6%) fully cover corresponding topological modules, and most disease modules (88.5%) are fully covered by the corresponding functional modules. Furthermore, we identified several protein modules of interest that we describe in detail, which demonstrate the power of our integrative approach. This approach allows us to link genes, and pathways with their corresponding disorders, which may ultimately help us to improve the prevention, diagnosis and treatment of disease.
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