scholarly journals Network structure of resource use and niche overlap within the endophytic microbiome

2021 ◽  
Author(s):  
Matthew Michalska-Smith ◽  
Zewei Song ◽  
Seth A. Spawn-Lee ◽  
Zoe A. Hansen ◽  
Mitch Johnson ◽  
...  
Keyword(s):  
Network Structure ◽  
Niche Overlap ◽  
Higher Order ◽  
Global Network ◽  
Network Analyses ◽  
Competitive Network ◽  
Weak Evidence ◽  
Bacteria And Fungi ◽  
Nutrient Amendment ◽  
And Function

AbstractEndophytes often have dramatic effects on their host plants. Characterizing the relationships among members of these communities has focused on identifying the effects of single microbes on their host, but has generally overlooked interactions among the myriad microbes in natural communities as well as potential higher-order interactions. Network analyses offer a powerful means for characterizing patterns of interaction among microbial members of the phytobiome that may be crucial to mediating its assembly and function. We sampled twelve endophytic communities, comparing patterns of niche overlap between coexisting bacteria and fungi to evaluate the effect of nutrient supplementation on local and global competitive network structure. We found that, despite differences in the degree distribution, there were few significant differences in the global network structure of niche-overlap networks following persistent nutrient amendment. Likewise, we found idiosyncratic and weak evidence for higher-order interactions regardless of nutrient treatment. This work provides a first-time characterization of niche-overlap network structure in endophytic communities and serves as a framework for higher-resolution analyses of microbial interaction networks as a consequence and a cause of ecological variation in microbiome function.

scholarly journals Competitive network determines the direction of the diversity–function relationship

2017 ◽  
Vol 114 (43) ◽  
pp. 11464-11469 ◽  
Author(s):  
Daniel S. Maynard ◽  
Thomas W. Crowther ◽  
Mark A. Bradford
Keyword(s):  
Species Diversity ◽  
Network Structure ◽  
Negative Relationship ◽  
Wood Decay ◽  
Experimental System ◽  
Community Level ◽  
Community Function ◽  
Competitive Network ◽  
Function Relationship ◽  
And Function

The structure of the competitive network is an important driver of biodiversity and coexistence in natural communities. In addition to determining which species survive, the nature and intensity of competitive interactions within the network also affect the growth, productivity, and abundances of those individuals that persist. As such, the competitive network structure may likewise play an important role in determining community-level functioning by capturing the net costs of competition. Here, using an experimental system comprising 18 wood decay basidiomycete fungi, we test this possibility by quantifying the links among competitive network structure, species diversity, and community function. We show that species diversity alone has negligible impacts on community functioning, but that diversity interacts with two key properties of the competitive network—competitive intransitivity and average competitive ability—to ultimately shape biomass production, respiration, and carbon use efficiency. Most notably, highly intransitive communities comprising weak competitors exhibited a positive diversity–function relationship, whereas weakly intransitive communities comprising strong competitors exhibited a negative relationship. These findings demonstrate that competitive network structure can be an important determinant of community-level functioning, capturing a gradient from weakly to strongly competitive communities. Our research suggests that the competitive network may therefore act as a unifying link between diversity and function, providing key insight as to how and when losses in biodiversity will impact ecosystem function.

scholarly journals Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bastian David ◽  
Jasmine Eberle ◽  
Daniel Delev ◽  
Jennifer Gaubatz ◽  
Conrad C. Prillwitz ◽  
...  
Keyword(s):  
Visual Field ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Diffusion Tensor ◽  
Global Network ◽  
Visual Field Defects ◽  
Whole Brain ◽  
Network Analyses ◽  
Postoperative Mri ◽  
Selective Amygdalohippocampectomy

AbstractSelective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyses from voxel- to network-level. Twenty-eight patients with temporal lobe epilepsy underwent pre- and postoperative MRI (T1-MPRAGE and Diffusion Tensor Imaging) as well as kinetic perimetry according to Goldmann standard. We probed for whole-brain gray matter (GM) and white matter (WM) correlates of VFD using voxel-based morphometry and tract-based spatial statistics, respectively. We furthermore reconstructed individual structural connectomes and conducted local and global network analyses. Two clusters in the bihemispheric middle temporal gyri indicated a postsurgical GM volume decrease with increasing VFD severity (FWE-corrected p < 0.05). A single WM cluster showed a fractional anisotropy decrease with increasing severity of VFD in the ipsilesional optic radiation (FWE-corrected p < 0.05). Furthermore, patients with (vs. without) VFD showed a higher number of postoperative local connectivity changes. Neither in the GM, WM, nor in network metrics we found preoperative correlates of VFD severity. Still, in an explorative analysis, an artificial neural network meta-classifier could predict the occurrence of VFD based on presurgical connectomes above chance level.

scholarly journals Network-based approaches to quantify multicellular development

2017 ◽  
Vol 14 (135) ◽  
pp. 20170484 ◽  
Author(s):  
Matthew D. B. Jackson ◽  
Salva Duran-Nebreda ◽  
George W. Bassel
Keyword(s):  
Structure Function ◽  
Spatial Relations ◽  
Higher Order ◽  
Cellular Interaction ◽  
Cellular Organization ◽  
Multicellular Development ◽  
Cell Organization ◽  
Function Relationship ◽  
And Function ◽  
The Relationship

Multicellularity and cellular cooperation confer novel functions on organs following a structure–function relationship. How regulated cell migration, division and differentiation events generate cellular arrangements has been investigated, providing insight into the regulation of genetically encoded patterning processes. Much less is known about the higher-order properties of cellular organization within organs, and how their functional coordination through global spatial relations shape and constrain organ function. Key questions to be addressed include: why are cells organized in the way they are? What is the significance of the patterns of cellular organization selected for by evolution? What other configurations are possible? These may be addressed through a combination of global cellular interaction mapping and network science to uncover the relationship between organ structure and function. Using this approach, global cellular organization can be discretized and analysed, providing a quantitative framework to explore developmental processes. Each of the local and global properties of integrated multicellular systems can be analysed and compared across different tissues and models in discrete terms. Advances in high-resolution microscopy and image analysis continue to make cellular interaction mapping possible in an increasing variety of biological systems and tissues, broadening the further potential application of this approach. Understanding the higher-order properties of complex cellular assemblies provides the opportunity to explore the evolution and constraints of cell organization, establishing structure–function relationships that can guide future organ design.

The Location and Global Network Structure of Maritime Advanced Producer Services

Urban Studies ◽  
2011 ◽  
Vol 48 (13) ◽  
pp. 2749-2769 ◽  
Author(s):  
Wouter Jacobs ◽  
Hans Koster ◽  
Peter Hall
Keyword(s):  
Network Structure ◽  
Global Network ◽  
Producer Services ◽  
Advanced Producer Services

Complex Autocatalysis in Simple Chemistries

2016 ◽  
Vol 22 (2) ◽  
pp. 138-152 ◽  
Author(s):  
Nathaniel Virgo ◽  
Takashi Ikegami ◽  
Simon McGregor
Keyword(s):  
Network Structure ◽  
Exponential Growth ◽  
Origins Of Life ◽  
Higher Order ◽  
Nonlinear Phenomena ◽  
Direct Reaction ◽  
Form Complex ◽  
First Order ◽  
Life On Earth ◽  
Catalytic Effects

Life on Earth must originally have arisen from abiotic chemistry. Since the details of this chemistry are unknown, we wish to understand, in general, which types of chemistry can lead to complex, lifelike behavior. Here we show that even very simple chemistries in the thermodynamically reversible regime can self-organize to form complex autocatalytic cycles, with the catalytic effects emerging from the network structure. We demonstrate this with a very simple but thermodynamically reasonable artificial chemistry model. By suppressing the direct reaction from reactants to products, we obtain the simplest kind of autocatalytic cycle, resulting in exponential growth. When these simple first-order cycles are prevented from forming, the system achieves superexponential growth through more complex, higher-order autocatalytic cycles. This leads to nonlinear phenomena such as oscillations and bistability, the latter of which is of particular interest regarding the origins of life.

scholarly journals Taxonomic Compositions and Co-occurrence Relationships of Protists in Bulk Soil and Rhizosphere of Soybean Fields in Different Regions of China

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Zhang ◽  
Pengcheng Xing ◽  
Mengyu Niu ◽  
Gehong Wei ◽  
Peng Shi
Keyword(s):  
High Throughput Sequencing ◽  
Niche Overlap ◽  
Spatial Location ◽  
Bulk Soil ◽  
Community Structures ◽  
Potential Predator ◽  
Predator Prey ◽  
Soybean Plants ◽  
Bacteria And Fungi ◽  
Potential Interactions

As the main consumers of bacteria and fungi in farmed soils, protists remain poorly understood. The aim of this study was to explore protist community assembly and ecological roles in soybean fields. Here, we investigated differences in protist communities using high-throughput sequencing and their inferred potential interactions with bacteria and fungi between the bulk soil and rhizosphere compartments of three soybean cultivars collected from six ecological regions in China. Distinct protist community structures characterized the bulk soil and rhizosphere of soybean plants. A significantly higher relative abundance of phagotrophs was observed in the rhizosphere (25.1%) than in the bulk soil (11.3%). Spatial location (R2 = 0.37–0.51) explained more of the variation in protist community structures of soybean fields than either the compartment (R2 = 0.08–0.09) or cultivar type (R2 = 0.02–0.03). The rhizosphere protist network (76 nodes and 414 edges) was smaller and less complex than the bulk soil network (147 nodes and 880 edges), indicating a smaller potential of niche overlap and interactions in the rhizosphere due to the increased resources in the rhizosphere. Furthermore, more inferred potential predator-prey interactions occur in the rhizosphere. We conclude that protists have a crucial ecological role to play as an integral part of microbial co-occurrence networks in soybean fields.

Utilizing Feature Selection on Higher Order Neural Networks

2016 ◽  
pp. 1099-1114
Author(s):  
Zongyuan Zhao ◽  
Shuxiang Xu ◽  
Byeong Ho Kang ◽  
Mir Md Jahangir Kabir ◽  
Yunling Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Prediction Accuracy ◽  
Higher Order ◽  
Computational Time ◽  
Learning Capabilities ◽  
Higher Order Neural Networks ◽  
And Function ◽  
Selection Algorithms ◽  
Fully Connected

Artificial Neural Network has shown its impressive ability on many real world problems such as pattern recognition, classification and function approximation. An extension of ANN, higher order neural network (HONN), improves ANN's computational and learning capabilities. However, the large number of higher order attributes leads to long learning time and complex network structure. Some irrelevant higher order attributes can also hinder the performance of HONN. In this chapter, feature selection algorithms will be used to simplify HONN architecture. Comparisons of fully connected HONN with feature selected HONN demonstrate that proper feature selection can be effective on decreasing number of inputs, reducing computational time, and improving prediction accuracy of HONN.

scholarly journals Ego-zones: non-symmetric dependencies reveal network groups with large and dense overlaps

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Milos Kudelka ◽  
Eliska Ochodkova ◽  
Sarka Zehnalova ◽  
Jakub Plesnik
Keyword(s):  
Network Structure ◽  
Real World ◽  
Ground Truth ◽  
Structural Similarity ◽  
Global Network ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
First Case ◽  
New Perspective ◽  
The Individual

Abstract The existence of groups of nodes with common characteristics and the relationships between these groups are important factors influencing the structures of social, technological, biological, and other networks. Uncovering such groups and the relationships between them is, therefore, necessary for understanding these structures. Groups can either be found by detection algorithms based solely on structural analysis or identified on the basis of more in-depth knowledge of the processes taking place in networks. In the first case, these are mainly algorithms detecting non-overlapping communities or communities with small overlaps. The latter case is about identifying ground-truth communities, also on the basis of characteristics other than only network structure. Recent research into ground-truth communities shows that in real-world networks, there are nested communities or communities with large and dense overlaps which we are not yet able to detect satisfactorily only on the basis of structural network properties.In our approach, we present a new perspective on the problem of group detection using only the structural properties of networks. Its main contribution is pointing out the existence of large and dense overlaps of detected groups. We use the non-symmetric structural similarity between pairs of nodes, which we refer to as dependency, to detect groups that we call zones. Unlike other approaches, we are able, thanks to non-symmetry, accurately to describe the prominent nodes in the zones which are responsible for large zone overlaps and the reasons why overlaps occur. The individual zones that are detected provide new information associated in particular with the non-symmetric relationships within the group and the roles that individual nodes play in the zone. From the perspective of global network structure, because of the non-symmetric node-to-node relationships, we explore new properties of real-world networks that describe the differences between various types of networks.

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