Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
There is a lack of understanding about the bacterial, fungal and archaeal communities' composition of solid-phase denitrification (SPD) systems. We investigated four SPD systems with different carbon sources by analyzing microbial gene sequences based on operational taxonomic unit (OTU) and amplicon sequence variant (ASV). The results showed that the corncob-polyvinyl alcohol sodium alginate-polycaprolactone (CPSP, 0.86±0.04 mg NO-N/(g·day)) and corncob (0.85±0.06 mg NO-N/(g·day)) had better denitrification efficiency than polycaprolactone (PCL, 0.29±0.11 mg NO-N/(g·day)) and polyvinyl alcohol-sodium alginate (PVA-SA, 0.24±0.07 mg NO-N/(g·day)). The bacterial, fungal and archaeal microbial composition was significantly different among carbon source types such as Proteobacteria in PCL (OTU: 83.72%, ASV: 82.49%) and Rozellomycota in PVA-SA (OTU: 71.99%, ASV: 81.30%). ASV methods can read more microbial units than that of OTU and exhibit higher alpha diversity and classify some species that had not been identified by OTU such as Nanoarchaeota phylum, unclassified_ f_ Xanthobacteraceae genus, etc., indicating ASV may be more conducive to understand SPD microbial communities. The co-occurring network showed some correlation between the bacteria fungi and archaea species, indicating different species may collaborate in SPD systems. Similar KEGG function prediction results were obtained in two bioinformatic methods generally and some fungi and archaea functions should not be ignored in SPD systems. These results may be beneficial for understanding microbial communities in SPD systems.
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Source |
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http://dx.doi.org/10.1016/j.jes.2023.08.008 | DOI Listing |
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