Succession and Catabolic Properties of Fungal Community During Composting of Fruit Waste at Sub-Tropical Environment

2021 ◽  
Author(s):  
Syeda Abeer Danish ◽  
Tooba Haq ◽  
Irum Liaqat ◽  
Saima Rubab ◽  
Mohammad Qureshi ◽  
...  
Keyword(s):  
Microbial Community ◽  
Fungal Community ◽  
Time Series Data ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Abiotic Factors ◽  
Organic Fertilizer ◽  
Illumina Miseq ◽  
Series Data ◽  
Molecular Technique ◽  
Fruit Waste

Abstract A comprehensive profile of structural and functional dynamics of fungal community during fruit waste composting was investigated. For this purpose, fruit waste was composted in a windrow setup. Representative samples were collected at varied range of incubation temperatures during composting period and employed to physicochemical and microbiological culture dependent and independent analysis. Time-series data revealed that variation in fungal load is temperature dependent that influenced morphotypes’ shifts during different stages of composting. Shifts in abiotic factors, availability of accessible nutrients, water loss, pH and electrical conductivity participated in the transition of community and compost maturity. Culture-based analysis showed rich microbial compost community, dominant with Aspergillus, Mucor, Rhizopus and Penicillium. Denaturing gradient gel electrophoresis analyses demonstrated the prevalence of diverse community in compost with detectable bands corresponding to Penicillium at mesophilic temperature while undetectable bands corresponding for Aspergillus. Succession in microbial community was observed during composting as with temperature variations. Illumina Miseq revealed fungal diversity including Mortierella sp from phylum Zygomycota as the most dominant fungi and Coprinopsis sp as second dominant from Basidiomycota, mainly associated with lignocellulosic degradation. Moreover, Aspergillus fumigatus (ADIF1) was found as the most promising cellulase and pectinase producers at higher temperature showing its potential for efficient environmental management utilization. Current findings suggest that transformation of fruit waste into seed germination friendly compost that can be used as an efficient organic fertilizer and incorporation of sensitive molecular technique suggests the transition of microbial community and improvement in microbial diversity.

scholarly journals Pumpkin powdery mildew disease severity influences the fungal diversity of the phyllosphere

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4559 ◽  
Author(s):  
Zhuo Zhang ◽  
Luyun Luo ◽  
Xinqiu Tan ◽  
Xiao Kong ◽  
Jianguo Yang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Powdery Mildew ◽  
Disease Severity ◽  
Fungal Community ◽  
Abiotic Factors ◽  
Its Region ◽  
Illumina Miseq ◽  
Fungal Community Structure ◽  
Powdery Mildew Disease ◽  
And Function

Phyllosphere microbiota play a crucial role in plant-environment interactions and their microbial community and function are influenced by biotic and abiotic factors. However, there is little research on how pathogens affect the microbial community of phyllosphere fungi. In this study, we collected 16 pumpkin (Cucurbita moschata) leaf samples which exhibited powdery mildew disease, with a severity ranging from L1 (least severe) to L4 (most severe). The fungal community structure and diversity was examined by Illumina MiSeq sequencing of the internal transcribed spacer (ITS) region of ribosomal RNA genes. The results showed that the fungal communities were dominated by members of the Basidiomycota and Ascomycota. ThePodosphaerawas the most dominant genus on these infected leaves, which was the key pathogen responsible for the pumpkin powdery mildew. The abundance of Ascomycota andPodosphaeraincreased as disease severity increased from L1 to L4, and was significantly higher at disease severity L4 (P< 0.05). The richness and diversity of the fungal community increased from L1 to L2, and then declined from L2 to L4, likely due to the biotic pressure (i.e., symbiotic and competitive stresses among microbial species) at disease severity L4. Our results could give new perspectives on the changes of the leaf microbiome at different pumpkin powdery mildew disease severity.

scholarly journals System Performance Corresponding to Bacterial Community Succession after a Disturbance in an Autotrophic Nitrogen Removal Bioreactor

mSystems ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Hsiao-Pei Lu ◽  
Yung-Hsien Shao ◽  
Jer-Horng Wu ◽  
Chih-hao Hsieh
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
System Performance ◽  
Time Series Data ◽  
Microbial Consortia ◽  
Series Data ◽  
Resolution Time ◽  
Physicochemical Variables ◽  
System Functioning ◽  
Fine Resolution

ABSTRACT Performance of a bioreactor is affected by complex microbial consortia that regulate system functional processes. Studies so far, however, have mainly emphasized the selective pressures imposed by operational conditions (i.e., deterministic external physicochemical variables) on the microbial community as well as system performance, but have overlooked direct effects of the microbial community on system functioning. Here, using a bioreactor with ammonium as the sole substrate under controlled operational settings as a model system, we investigated succession of the bacterial community after a disturbance and its impact on nitrification and anammox (anaerobic ammonium oxidation) processes with fine-resolution time series data. System performance was quantified as the ratio of the fed ammonium converted to anammox-derived nitrogen gas (N2) versus nitrification-derived nitrate (npNO3−). After the disturbance, the N2/npNO3− ratio first decreased, then recovered, and finally stabilized until the end. Importantly, the dynamics of N2/npNO3− could not be fully explained by physicochemical variables of the system. In comparison, the proportion of variation that could be explained substantially increased (tripled) when the changes in bacterial composition were taken into account. Specifically, distinct bacterial taxa tended to dominate at different successional stages, and their relative abundances could explain up to 46% of the variation in nitrogen removal efficiency. These findings add baseline knowledge of microbial succession and emphasize the importance of monitoring the dynamics of microbial consortia for understanding the variability of system performance. IMPORTANCE Dynamics of microbial communities are believed to be associated with system functional processes in bioreactors. However, few studies have provided quantitative evidence. The difficulty of evaluating direct microbe-system relationships arises from the fact that system performance is affected by convolved effects of microbiota and bioreactor operational parameters (i.e., deterministic external physicochemical forcing). Here, using fine-resolution time series data (daily sampling for 2 months) under controlled operational settings, we performed an in-depth analysis of system performance as a function of the microbial community in the context of bioreactor physicochemical conditions. We obtained statistically evaluated results supporting the idea that monitoring microbial community dynamics could improve the ability to predict system functioning, beyond what could be explained by operational physicochemical variables. Moreover, our results suggested that considering the succession of multiple bacterial taxa would account for more system variation than focusing on any particular taxon, highlighting the need to integrate microbial community ecology for understanding system functioning.

scholarly journals Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

2011 ◽  
Vol 5 (Suppl 2) ◽  
pp. S15 ◽  
Author(s):  
Li C Xia ◽  
Joshua A Steele ◽  
Jacob A Cram ◽  
Zoe G Cardon ◽  
Sheri L Simmons ◽  
...  
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
Time Series Data ◽  
Series Data ◽  
Similarity Analysis ◽  
Local Similarity

scholarly journals Pumpkin powdery mildew disease severity influences the fungal diversity of the phyllosphere

2017 ◽  
Author(s):  
Zhuo Zhang ◽  
Luyun Luo ◽  
Xinqiu Tan ◽  
Xiao Kong ◽  
Jianguo Yang ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Disease Severity ◽  
Fungal Community ◽  
Abiotic Factors ◽  
Its Region ◽  
Illumina Miseq ◽  
Fungal Community Structure ◽  
Plant Environment ◽  
Powdery Mildew Disease ◽  
Rna Genes

Phyllosphere microbiota play a crucial role in plant-environment interactions and are influenced by biotic and abiotic factors. However, there is little research on how pathogen s affect the microbial community. In this study, we collected 16 pumpkin (Cucurbita moschata) leaf samples showing symptoms of powdery mildew disease with different disease severity levels ranging from L1 (least severe) to L4 (most severe). We examined the fungal community structure and diversity by Illumina MiSeq sequencing of the internal transcribed spacer (ITS) region of ribosomal RNA genes. The fungal communities were dominated by members of the Basidiomycota and Ascomycota. The dominant genus was Podosphaera on the diseased leaves, which was the key pathogen responsible for the pumpkin powdery mildew. Ascomycota and Podosphaera increased in abundance as disease severity increased from L1 to L4, and were significantly more abundant than other microorganisms at disease severity L4 (P<0.05). The richness and diversity of the fungal community increased from L1 to L2, and then declined from L2 to L4, likely due to the biotic pressure at disease severity L4. Maintaining species richness in the phyllosphere will be an important part of managing disease control in this agroecological system and an essential step toward predictable biocontrol of powdery mildew in pumpkin.

scholarly journals The Influence of Residue Mixing on the Decomposition of Pepper Root Residues

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 84
Author(s):  
Xianhong Zhang ◽  
Zhilin Wang ◽  
Fengzhi Wu ◽  
Xingang Zhou
Keyword(s):  
Microbial Community ◽  
Mass Loss ◽  
Fungal Community ◽  
Microbial Community Composition ◽  
Terrestrial Ecosystems ◽  
Illumina Miseq ◽  
Amplicon Sequencing ◽  
Nitrogen And Phosphorus ◽  
Residue Decomposition ◽  
Degradation Rates

(1) Background: Residue degradation plays a very important role in terrestrial ecosystems and residue mixing is the main factor affecting the degradation rates. However, in the agricultural systems, the effect of residue mixing on the degradation of pepper residues and the microbial community in pepper root residues is not clear. (2) Methods: In this study, we added different residues into soil by using double-layered nylon litterbags in culture bottles. The treatments including pepper root (P: Capsicum annuum L.), soybean [S: Glycine max (L.) Merr.] and maize (M: Zea mays L.) residue, as well as mixtures of soybean + pepper (SP), maize + pepper (MP), maize + soybean + pepper (MSP) mixtures. Litterbags were harvested after 7, 14, 28, and 56 days, respectively. Mass loss and nitrogen and phosphorus contents in pepper residue were quantified and bacterial and fungal community levels in pepper residues were analyzed using quantitative PCR and high throughput amplicon sequencing; (3) Results: The study showed that the mass loss and fungal community abundance of pepper root residue in mixtures were higher than P, except day 7. The phosphorus contents in MSP-P and MP-P were significantly lower than that for P at day 28 and day 56. Illumina MiSeq sequencing showed that the presence of maize residue significantly altered the microbial community composition of pepper root pepper. Day 56. (4) Conclusions: Our results suggest that residue mixing changed the microbial community abundance in pepper residue and promoted the degradation of pepper residues compared to pepper residue decomposition alone, especially for mixtures with soybean.

scholarly journals Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 216 ◽  
Author(s):  
Dongmei Ai ◽  
Xiaoxin Li ◽  
Gang Liu ◽  
Xiaoyi Liang ◽  
Li Xia
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
Granger Causality ◽  
Large Scale ◽  
Community Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Association Network ◽  
Causal Relationships ◽  
Network Analyses

The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

scholarly journals Detecting alternative attractors in ecosystem dynamics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Torbjörn Säterberg ◽  
Kevin McCann
Keyword(s):  
Population Dynamics ◽  
Time Series Data ◽  
Abiotic Factors ◽  
Natural Populations ◽  
Ecosystem Dynamics ◽  
Series Data ◽  
Natural Systems ◽  
Experimental Systems ◽  
Dynamical Behaviors ◽  
Natural Time

AbstractDynamical systems theory suggests that ecosystems may exhibit alternative dynamical attractors. Such alternative attractors, as for example equilibria and cycles, have been found in the dynamics of experimental systems. Yet, for natural systems, where multiple biotic and abiotic factors simultaneously affect population dynamics, it is more challenging to distinguish alternative dynamical behaviors. Although recent research exemplifies that some natural systems can exhibit alternative states, a robust methodology for testing whether these constitute distinct dynamical attractors is currently lacking. Here, using attractor reconstruction techniques we develop such a test. Applications of the methodology to simulated, experimental and natural time series data, reveal that alternative dynamical behaviors are hard to distinguish if population dynamics are governed by purely stochastic processes. However, if population dynamics are brought about also by mechanisms internal to the system, alternative attractors can readily be detected. Since many natural populations display evidence of such internally driven dynamics, our approach offers a method for empirically testing whether ecosystems exhibit alternative dynamical attractors.

scholarly journals Pumpkin powdery mildew disease severity influences the fungal diversity of the phyllosphere

2017 ◽  
Author(s):  
Zhuo Zhang ◽  
Luyun Luo ◽  
Xinqiu Tan ◽  
Xiao Kong ◽  
Jianguo Yang ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Disease Severity ◽  
Fungal Community ◽  
Abiotic Factors ◽  
Its Region ◽  
Illumina Miseq ◽  
Fungal Community Structure ◽  
Plant Environment ◽  
Powdery Mildew Disease ◽  
Rna Genes

Phyllosphere microbiota play a crucial role in plant-environment interactions and are influenced by biotic and abiotic factors. However, there is little research on how pathogen s affect the microbial community. In this study, we collected 16 pumpkin (Cucurbita moschata) leaf samples showing symptoms of powdery mildew disease with different disease severity levels ranging from L1 (least severe) to L4 (most severe). We examined the fungal community structure and diversity by Illumina MiSeq sequencing of the internal transcribed spacer (ITS) region of ribosomal RNA genes. The fungal communities were dominated by members of the Basidiomycota and Ascomycota. The dominant genus was Podosphaera on the diseased leaves, which was the key pathogen responsible for the pumpkin powdery mildew. Ascomycota and Podosphaera increased in abundance as disease severity increased from L1 to L4, and were significantly more abundant than other microorganisms at disease severity L4 (P<0.05). The richness and diversity of the fungal community increased from L1 to L2, and then declined from L2 to L4, likely due to the biotic pressure at disease severity L4. Maintaining species richness in the phyllosphere will be an important part of managing disease control in this agroecological system and an essential step toward predictable biocontrol of powdery mildew in pumpkin.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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