Background: Ponds are important freshwater habitats that support both human and environmental activities. However, relative to their larger counterparts (e.g. rivers, lakes), ponds are understudied, especially with regard to their microbial communities. Our study aimed to fill this knowledge gap by using culture-independent, high-throughput sequencing to assess the dynamics, taxonomy, and functionality of bacterial and viral communities in a freshwater agricultural pond.
Results: Water samples (n = 14) were collected from a Mid-Atlantic agricultural pond between June 2017 and May 2018 and filtered sequentially through 1 and 0.2 μm filter membranes. Total DNA was then extracted from each filter, pooled, and subjected to 16S rRNA gene and shotgun sequencing on the Illumina HiSeq 2500 platform. Additionally, on eight occasions water filtrates were processed for viral metagenomes (viromes) using chemical concentration and then shotgun sequenced. A ubiquitous freshwater phylum, Proteobacteria was abundant at all sampling dates throughout the year. However, environmental characteristics appeared to drive the structure of the community. For instance, the abundance of Cyanobacteria (e.g. Nostoc) increased with rising water temperatures, while a storm event appeared to trigger an increase in overall bacterial diversity, as well as the relative abundance of Bacteroidetes. This event was also associated with an increase in the number of antibiotic resistance genes. The viral fractions were dominated by dsDNA of the order Caudovirales, namely Siphoviridae and Myovirdae.
Conclusions: Overall, this study provides one of the largest datasets on pond water microbial ecology to date, revealing seasonal trends in the microbial taxonomic composition and functional potential.
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http://dx.doi.org/10.1186/s40793-020-00365-8 | DOI Listing |
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The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Sensors (Basel)
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Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
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January 2025
School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Breast cancer (BC) is one of the most lethal cancers worldwide, and its early diagnosis is critical for improving patient survival rates. However, the extraction of key information from complex medical images and the attainment of high-precision classification present a significant challenge. In the field of signal processing, texture-rich images typically exhibit periodic patterns and structures, which are manifested as significant energy concentrations at specific frequencies in the frequency domain.
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December 2024
College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
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Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process.
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