Forest fires can destroy forest and inflict great damage to the ecosystem. Fortunately, forest fire detection with video has achieved remarkable results in enabling timely and accurate fire warnings. ...However, the traditional forest fire detection method relies heavily on artificially designed features; CNN-based methods require a large number of parameters. In addition, forest fire detection is easily disturbed by fog. To solve these issues, a lightweight YOLOX-L and defogging algorithm-based forest fire detection method, GXLD, is proposed. GXLD uses the dark channel prior to defog the image to obtain a fog-free image. After the lightweight improvement of YOLOX-L by GhostNet, depth separable convolution, and SENet, we obtain the YOLOX-L-Light and use it to detect the forest fire in the fog-free image. To evaluate the performance of YOLOX-L-Light and GXLD, mean average precision (mAP) was used to evaluate the detection accuracy, and network parameters were used to evaluate the lightweight effect. Experiments on our forest fire dataset show that the number of the parameters of YOLOX-L-Light decreased by 92.6%, and the mAP increased by 1.96%. The mAP of GXLD is 87.47%, which is 2.46% higher than that of YOLOX-L; and the average fps of GXLD is 26.33 when the input image size is 1280 × 720. Even in a foggy environment, the GXLD can detect a forest fire in real time with a high accuracy, target confidence, and target integrity. This research proposes a lightweight forest fire detection method (GXLD) with fog removal. Therefore, GXLD can detect a forest fire with a high accuracy in real time. The proposed GXLD has the advantages of defogging, a high target confidence, and a high target integrity, which makes it more suitable for the development of a modern forest fire video detection system.
Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a ...microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.
Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).
The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.
Discerning network interactions among different species/populations in microbial communities has evoked substantial interests in recent years, but little information is available about temporal ...dynamics of microbial network interactions in response to environmental perturbations. Here, we modified the random matrix theory‐based network approach to discern network succession in groundwater microbial communities in response to emulsified vegetable oil (EVO) amendment for uranium bioremediation. Groundwater microbial communities from one control and seven monitor wells were analysed with a functional gene array (GeoChip 3.0), and functional molecular ecological networks (fMENs) at different time points were reconstructed. Our results showed that the network interactions were dramatically altered by EVO amendment. Dynamic and resilient succession was evident: fairly simple at the initial stage (Day 0), increasingly complex at the middle period (Days 4, 17, 31), most complex at Day 80, and then decreasingly complex at a later stage (140–269 days). Unlike previous studies in other habitats, negative interactions predominated in a time‐series fMEN, suggesting strong competition among different microbial species in the groundwater systems after EVO injection. Particularly, several keystone sulfate‐reducing bacteria showed strong negative interactions with their network neighbours. These results provide mechanistic understanding of the decreased phylogenetic diversity during environmental perturbations.
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•Microbial networks were distinctly different among seasons.•Networks with higher complexity and more keystone taxa exhibited higher stability.•Water temperature and pH influenced ...microbial networks and community stability.•Keystone taxa could be regarded as the indicators of community stability.
Seasonal variations of environmental factors generally result in considerable changes in microbiomes, yet we still lack a clear understanding of how microbial communities maintain their ecological stability. Here, we analyze a long-term (nine years) high-throughput sequencing dataset by network analysis to illustrate how the microbial stability varies among seasons in lake ecosystems. The results showed that the microbial networks were distinctly different among seasons, and the most complex network in autumn exhibited the highest stability. We found that the strong connecting structures among microbes (large modules) were significantly (P < 0.05) correlated with water temperature and pH, which would negatively affect keystone taxa and destabilize microbial communities. The networks with more keystone taxa were generally stable and the removal of keystone taxa would result in unstable networks. Moreover, the keystone taxa were specific among seasons due to environmental filtering and relevant interspecific interactions. These results suggested that seasonal variations of environmental factors could influence microbial networks and community stability via the regulation of keystone taxa. Thus, the keystone taxa could be used as indicators to reflect the seasonal stability of microbial communities in lake ecosystems. These findings improve our understanding of the microbial seasonal co-occurrence patterns and reveal the mechanism how microbial communities maintain their seasonal stability in aquatic ecosystems.
Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO(2), is a central goal in ecology but is poorly ...understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO(2) enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO(2) levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management.
The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO(2) level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.
Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, ...over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied "open-format" and "closed-format" detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications and focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions.
Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on "species" richness and abundance but not ...on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO(2) (eCO(2)) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO(2) enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO(2) and ambient CO(2) (aCO(2)) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO(2) and aCO(2), at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO(2) dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change.
Ion hydration and transport at interfaces are relevant to a wide range of applied fields and natural processes
. Interfacial effects are particularly profound in confined geometries such as ...nanometre-sized channels
, where the mechanisms of ion transport in bulk solutions may not apply
. To correlate atomic structure with the transport properties of hydrated ions, both the interfacial inhomogeneity and the complex competing interactions among ions, water and surfaces require detailed molecular-level characterization. Here we constructed individual sodium ion (Na
) hydrates on a NaCl(001) surface by progressively attaching single water molecules (one to five) to the Na
ion using a combined scanning tunnelling microscopy and noncontact atomic force microscopy system. We found that the Na
ion hydrated with three water molecules diffuses orders of magnitude more quickly than other ion hydrates. Ab initio calculations revealed that such high ion mobility arises from the existence of a metastable state, in which the three water molecules around the Na
ion can rotate collectively with a rather small energy barrier. This scenario would apply even at room temperature according to our classical molecular dynamics simulations. Our work suggests that anomalously high diffusion rates for specific hydration numbers of ions are generally determined by the degree of symmetry match between the hydrates and the surface lattice.
Unraveling the drivers of community structure and succession in response to environmental change is a central goal in ecology. Although the mechanisms shaping community structure have been ...intensively examined, those controlling ecological succession remain elusive. To understand the relative importance of stochastic and deterministic processes in mediating microbial community succession, a unique framework composed of four different cases was developed for fluidic and nonfluidic ecosystems. The framework was then tested for one fluidic ecosystem: a groundwater system perturbed by adding emulsified vegetable oil (EVO) for uranium immobilization. Our results revealed that groundwater microbial community diverged substantially away from the initial community after EVO amendment and eventually converged to a new community state, which was closely clustered with its initial state. However, their composition and structure were significantly different from each other. Null model analysis indicated that both deterministic and stochastic processes played important roles in controlling the assembly and succession of the groundwater microbial community, but their relative importance was time dependent. Additionally, consistent with the proposed conceptual framework but contradictory to conventional wisdom, the community succession responding to EVO amendment was primarily controlled by stochastic rather than deterministic processes. During the middle phase of the succession, the roles of stochastic processes in controlling community composition increased substantially, ranging from 81.3% to 92.0%. Finally, there are limited successional studies available to support different cases in the conceptual framework, but further well-replicated explicit time-series experiments are needed to understand the relative importance of deterministic and stochastic processes in controlling community succession.
Highlights ► Functional gene arrays (FGAs) target key genes for microbial functional processes. ► Summary of currently reported FGAs. ► FGAs have been demonstrated to be a specific, sensitive, and ...quantitative tool. ► FGAs can be used for rapidly analyzing microbial communities from different habitats. ► Key issues, challenges, and future directions in FGA technology are discussed.
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