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A very common way to classify bacteria is through microscopic images. Microscopic cell counting is a widely used technique to measure microbial growth. To date, fully automated methodologies are available for accurate and fast measurements; yet for bacteria dividing longitudinally, as in the case of Candidatus Thiosymbion oneisti, its cell count mainly remains manual. The identification of this type of cell division is important because it helps to detect undergoing cellular division from those which are not dividing once the sample is fixed. Our solution automates the classification of longitudinal division by using a machine learning method called residual network. Using transfer learning, we train a binary classification model in fewer epochs compared to the model trained without it. This potentially eliminates most of the manual labor of classifying the type of bacteria cell division. The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies. [ABSTRACT FROM AUTHOR]

Marine snow is an important habitat for microbes, characterized by chemical and physical properties contrasting those of the ambient water. The higher nutrient concentrations in marine snow lead to compositional differences between the ambient water and the marine snow-associated prokaryotic community. Whether these compositional differences vary due to seasonal environmental changes, however, remains unclear. Thus, we investigated the seasonal patterns of the free-living and marine snow-associated microbial community composition and their functional potential in the northern Adriatic Sea. Our data revealed seasonal patterns in both, the free-living and marine snow-associated prokaryotes. The two assemblages were more similar to each other in spring and fall than in winter and summer. The taxonomic distinctness resulted in a contrasting functional potential. Motility and adaptations to low temperature in winter and partly anaerobic metabolism in summer characterized the marine snow-associated prokaryotes. Free-living prokaryotes were enriched in genes indicative for functions related to phosphorus limitation in winter and in genes tentatively supplementing heterotrophic growth with proteorhodopsins and CO-oxidation in summer. Taken together, the results suggest a strong influence of environmental parameters on both free-living and marine snow-associated prokaryotic communities in spring and fall leading to higher similarity between the communities, while the marine snow habitat in winter and summer leads to a specific prokaryotic community in marine snow in these two seasons. [ABSTRACT FROM AUTHOR]

Summary: Messenger RNA can provide valuable insights into the variability of metabolic processes of microorganisms. However, due to uncertainties that include the stability of RNA, its application for activity profiling of environmental samples is questionable. We explored different factors affecting the decay rate of transcripts of three marine bacterial isolates using qPCR and determined mRNA half‐life time of specific bacterial taxa and of functional genes by metatranscriptomics of a coastal environmental prokaryotic community. The half‐life time of transcripts from 11 genes from bacterial isolates ranged from 1 to 46 min. About 80% of the analysed transcripts exhibited half‐live times shorter than 10 min. Significant differences were found in the half‐life time between mRNA and rRNA. The half‐life time of mRNA obtained from a coastal metatranscriptome ranged from 9 to 400 min. The shortest half‐life times of the metatranscriptome corresponded to transcripts from the same clusters of orthologous groups (COGs) in all bacterial classes. The prevalence of short mRNA half‐life time in genes related to defence mechanisms and motility indicate a tight connection of RNA decay rate to environmental stressors. The short half‐life time of RNA and its high variability needs to be considered when assessing metatranscriptomes especially in environmental samples. [ABSTRACT FROM AUTHOR]