scholarly journals Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danijela Šantić ◽  
Kasia Piwosz ◽  
Frano Matić ◽  
Ana Vrdoljak Tomaš ◽  
Jasna Arapov ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Adriatic Sea ◽  
Bacterial Community Composition ◽  
Environmental Data ◽  
Large Contribution ◽  
Neural Network Analysis

AbstractBacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was analysed, along with a depth profile, in the open Adriatic Sea using amplicon sequencing of bacterial 16S rRNA and the Neural gas algorithm. The performed analysis classified the sample into four best matching units representing heterogenic patterns of the bacterial community composition. The observed parameters were more differentiated by depth than by area, with temperature and identified salinity as important environmental variables. The highest diversity was observed at the deep chlorophyll maximum, while bacterial abundance and production peaked in the upper layers. The most of the identified genera belonged to Proteobacteria, with uncultured AEGEAN-169 and SAR116 lineages being dominant Alphaproteobacteria, and OM60 (NOR5) and SAR86 being dominant Gammaproteobacteria. Marine Synechococcus and Cyanobium-related species were predominant in the shallow layer, while Prochlorococcus MIT 9313 formed a higher portion below 50 m depth. Bacteroidota were represented mostly by uncultured lineages (NS4, NS5 and NS9 marine lineages). In contrast, Actinobacteriota were dominated by a candidatus genus Ca. Actinomarina. A large contribution of Nitrospinae was evident at the deepest investigated layer. Our results document that neural network analysis of environmental data may provide a novel insight into factors affecting picoplankton in the open sea environment.

Impacts of invasive plant species on soil biodiversity: a case study of dog-strangling vine (Vincetoxicum rossicum) in a Canadian National Park

2018 ◽  
Vol 98 (4) ◽  
pp. 716-723 ◽  
Author(s):  
Laura N. Bugiel ◽  
Stuart W. Livingstone ◽  
Marney E. Isaac ◽  
Roberta R. Fulthorpe ◽  
Adam R. Martin
Keyword(s):  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Invasive Plant ◽  
Bacterial Community Composition ◽  
Anthropogenic Disturbances ◽  
Soil Parameters ◽  
Invasive Plant Species ◽  
Vincetoxicum Rossicum ◽  
Soil Bacterial

Soil microbial diversity is expected to be altered by the establishment of invasive plant species, such as dog-strangling vine (DSV) [Vincetoxicum rossicum (Apocynaceae)]. However, in urban ecosystems where DSV invasion is high, there is little research evaluating the impacts of DSV and other anthropogenic disturbances on microbial diversity. Our study was based in Rouge National Urban Park, Canada, where we used terminal restriction fragment length polymorphism data to evaluate (i) if DSV has a detectable impact on soil bacterial community composition and (ii) if these impacts occur independently of other anthropogenic change or soil characteristics. Variation in soil bacterial communities was greatly reduced in DSV-invaded sites vs. less-invaded sites. The degree of DSV invasion independently explained 23.8% of variation in bacterial community composition: a value similar to the explanatory power of proximity to roadways (which explained 22.6% of the variation in community composition), and considerably greater than soil parameters (pH, moisture, carbon, and nitrogen concentrations) which explained only between 6.0% and 10.0% of variation in bacterial community composition. Our findings indicate that DSV influences soil bacterial community composition independent of other anthropogenic disturbances and soil parameters, with potential impacts on multiple facets of plant–soil interactions and plant invasion dynamics.

scholarly journals Pyrosequencing reveals transient cystic fibrosis lung microbiome changes with intravenous antibiotics

2014 ◽  
Vol 44 (4) ◽  
pp. 922-930 ◽  
Author(s):  
Daniel J. Smith ◽  
Alison C. Badrick ◽  
Martha Zakrzewski ◽  
Lutz Krause ◽  
Scott C. Bell ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Bacterial Community ◽  
Microbial Diversity ◽  
Community Composition ◽  
Relative Abundance ◽  
Bacterial Community Composition ◽  
Molecular Techniques ◽  
Pulmonary Exacerbation ◽  
Intravenous Antibiotics ◽  
The Impact

Chronic airway infection in adults with cystic fibrosis (CF) is polymicrobial and the impact of intravenous antibiotics on the bacterial community composition is poorly understood. We employed culture-independent molecular techniques to explore the early effects of i.v. antibiotics on the CF airway microbiome.DNA was extracted from sputum samples collected from adult subjects with CF at three time-points (before starting treatment, and at day 3 and day 8–10 of i.v. antibiotics) during treatment of an infective pulmonary exacerbation. Microbial community profiles were derived through analysis of bacterial-derived 16S ribosomal RNA by pyrosequencing and changes over time were compared.59 sputum samples were collected during 24 pulmonary exacerbations from 23 subjects. Between treatment onset and day 3 there was a significant reduction in the relative abundance of Pseudomonas and increased microbial diversity. By day 8–10, bacterial community composition was similar to pre-treatment. Changes in community composition did not predict improvements in lung function.The relative abundance of Pseudomonas falls rapidly in subjects with CF receiving i.v. antibiotic treatment for a pulmonary exacerbation and is accompanied by an increase in overall microbial diversity. However, this effect is not maintained beyond the first week of treatment.

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

NEURAL NETWORK ANALYSIS OF INTERACTION OF POSTURAL CONTROL MACHANISMS UNDER ARTUFICIAL FEEDBACK

2016 ◽  
Vol 34 (2) ◽  
pp. 025-036
Author(s):  
Oleg G. Gorshkov ◽  
◽  
Irina B. Starchenko ◽  
Andrey S. Sliva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Postural Control ◽  
Neural Network Analysis

scholarly journals Collection of Environmental Variables and Bacterial Community Compositions in Marian Cove, Antarctica, during Summer 2018

Data ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 27
Author(s):  
Hyo-Ryeon Kim ◽  
Jae-Hyun Lim ◽  
Ju-Hyoung Kim ◽  
Il-Nam Kim
Keyword(s):  
Bacterial Community ◽  
Community Composition ◽  
Marine Bacteria ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Bacterial Community Composition ◽  
Composition Data ◽  
Decomposition Of Organic Matter ◽  
Class Level ◽  
Earth’S Climate

Marine bacteria, which are known as key drivers for marine biogeochemical cycles and Earth’s climate system, are mainly responsible for the decomposition of organic matter and production of climate-relevant gases (i.e., CO₂, N₂O, and CH₄). However, research is still required to fully understand the correlation between environmental variables and bacteria community composition. Marine bacteria living in the Marian Cove, where the inflow of freshwater has been rapidly increasing due to substantial glacial retreat, must be undergoing significant environmental changes. During the summer of 2018, we conducted a hydrographic survey to collect environmental variables and bacterial community composition data at three different layers (i.e., the seawater surface, middle, and bottom layers) from 15 stations. Of all the bacterial data, 17 different phylum level bacteria and 21 different class level bacteria were found and Proteobacteria occupy 50.3% at phylum level following Bacteroidetes. Gammaproteobacteria and Alphaproteobacteria, which belong to Proteobacteria, are the highest proportion at the class level. Gammaproteobacteria showed the highest relative abundance in all three seawater layers. The collection of environmental variables and bacterial composition data contributes to improving our understanding of the significant relationships between marine Antarctic regions and marine bacteria that lives in the Antarctic.

scholarly journals Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

2021 ◽  
Author(s):  
Daniil A. Boiko ◽  
Evgeniy O. Pentsak ◽  
Vera A. Cherepanova ◽  
Evgeniy G. Gordeev ◽  
Valentine P. Ananikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Analysis ◽  
Carbon Material ◽  
Deep Neural Network ◽  
Carbon Materials ◽  
Material Surface ◽  
Neural Network Analysis ◽  
Pd Nanoparticle ◽  
Sem Imaging

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.

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